2496 lines
1.1 MiB
2496 lines
1.1 MiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem Set 8: Unsupervised Learning\n",
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"\n",
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"\n",
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"**Release Date:** 9 April 2024\n",
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"\n",
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"**Due Date:** 23:59, 20 April 2024\n",
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"\n",
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"In this problem set, we will be exploring unsupervised learning for image compression and classification. In particular, we will be implementing the K-Means algorithm, exploring its use for image compression, and experimenting with various unsupervised learning algorithms (specifically, K-Means and Principle Component Analysis) for the purpose of semi-supervised classification in the absence of labelled data.\n",
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"\n",
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"**Gentle reminder that there is penalty for using iterative method where numpy is possible. We have written down the number of loops needed in each of the task as a comment.**\n",
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"\n",
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"_Honour Code: Note that plagiarism will not be condoned! You may discuss with your classmates and check the internet for references, but you MUST NOT submit any code/report/explanation that is copied directly from other sources!_"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Set-Up\n",
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"\n",
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"In this section, packages and functions that are needed for the following tasks\n",
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"are specified. Please ensure that all code in this section is run before running\n",
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"other code snippets in this notebook.\n",
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"\n",
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"Note that you should **NOT** modify any code in this section. However, you\n",
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"might want to have a look at the helper functions specified so that you can use \n",
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"them whenever appropriate."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Imports\n",
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"\n",
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"The following lines of code import packages and functions that are necessary\n",
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"for the following tasks.\n",
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"\n",
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"As a reminder, please **do not** modify the following lines of code by adding,\n",
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"removing or modifying the specified imports. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-13T11:07:37.914218Z",
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"start_time": "2024-04-13T11:07:37.908299Z"
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}
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},
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"outputs": [],
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"source": [
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"import csv\n",
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"import os\n",
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"\n",
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"import matplotlib.image as mpimg\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.decomposition import PCA"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Helper Functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-13T11:07:38.040716Z",
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"start_time": "2024-04-13T11:07:38.035220Z"
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}
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},
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"outputs": [],
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"source": [
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"def load_rgb_image(image_file_path):\n",
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" '''\n",
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" Loads the RGB image at `image_file_path`, where the file path should be\n",
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" specified relative to this notebook, and returns the image as an `ndarray`\n",
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" with shape `(h, w, 3)`, where `h` and `w` are the height and width of the\n",
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" image respectively.\n",
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"\n",
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" NOTE: every entry in the returned `ndarray` is an integer between 0 and 255,\n",
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" inclusive.\n",
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" '''\n",
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"\n",
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" dirname = os.path.abspath('')\n",
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" image_path = os.path.join(dirname, image_file_path)\n",
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" image = mpimg.imread(image_path)\n",
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" return image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-13T11:07:38.074365Z",
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"start_time": "2024-04-13T11:07:38.070494Z"
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}
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},
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"outputs": [],
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"source": [
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"def display_image(image):\n",
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" '''\n",
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" Displays image that is represented by `image`, an `ndarray`.\n",
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"\n",
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" NOTE: if the data type of `image` is `int`, its entries should have values\n",
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" between 0 and 255 (inclusive); otherwise, its entries should have values\n",
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" between 0 and 1 (inclusive).\n",
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" '''\n",
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" plt.imshow(image)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-13T11:07:38.111720Z",
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"start_time": "2024-04-13T11:07:38.103629Z"
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}
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},
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"outputs": [],
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"source": [
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"def _load_digits_data(is_train):\n",
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" '''\n",
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" Loads handwritten digits dataset. \n",
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"\n",
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" Parameter\n",
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" ---------\n",
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" is_train: bool\n",
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" If `is_train` is `True`, the dataset returned will be unlabelled; otherwise,\n",
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" it is labelled\n",
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" \n",
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" Returns\n",
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" -------\n",
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" An `m * n` matrix `samples`. Here, `m` is the number of samples.\n",
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"\n",
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" If `is_train` is `True`, then `n` is equal to `h * w`, where `h` denotes the\n",
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" image height and `w` denotes the image width.\n",
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" '''\n",
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" dirname = os.path.abspath('')\n",
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" file_name = 'digits_train.csv' if is_train else 'digits_validation.csv'\n",
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" file_path = os.path.join(dirname, file_name)\n",
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" data = []\n",
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" \n",
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" with open(file_path, mode='r') as file:\n",
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" rows = csv.reader(file)\n",
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" for row in rows: \n",
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" data.append([int(num) for num in row])\n",
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"\n",
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" return np.array(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-13T11:07:38.178548Z",
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"start_time": "2024-04-13T11:07:38.172251Z"
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}
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},
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"outputs": [],
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"source": [
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"def load_digits_data_train():\n",
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" '''\n",
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" Loads the training dataset for the handwritten digits recognition problem.\n",
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"\n",
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" Returns\n",
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" -------\n",
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" A 2D array `digits`, where `digits[i].reshape((28, 28))` is the image of the `i`th \n",
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" handwritten digit. This image only has one channel, i.e. every pixel is\n",
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" only represented by an intensity value rather than an RGB triplet.\n",
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" '''\n",
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" return _load_digits_data(True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-13T11:07:38.260912Z",
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"start_time": "2024-04-13T11:07:38.255400Z"
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}
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},
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"outputs": [],
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"source": [
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"def load_digits_data_validation():\n",
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" '''\n",
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" Loads the validation dataset for the handwritten digits recognition problem.\n",
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"\n",
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" Returns\n",
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" -------\n",
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" A tuple (`digits`, `labels`). \n",
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"\n",
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" `digits` is a 2D array, where `digits[i].reshape((28, 28))` is the image of \n",
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" the `i`th handwritten digit. This image only has one channel, i.e. every pixel \n",
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" is only represented by an intensity value rather than an RGB triplet.\n",
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"\n",
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" `labels` is an array where `labels[i]` returns the actual label of the `i`th\n",
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" handwritten digit in `digits`. Note that `labels[i]` is an integer such that\n",
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" 0 <= `labels[i]` <= 9.\n",
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" '''\n",
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" data = _load_digits_data(False)\n",
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" digits = data[:, 1:]\n",
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" labels = data[:, 0]\n",
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" return digits, labels"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-13T11:07:38.277405Z",
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"start_time": "2024-04-13T11:07:38.272006Z"
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}
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},
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"outputs": [],
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"source": [
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"def compute_accuracy(pred_labels, true_labels):\n",
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" '''\n",
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" Computes the accuracy of the predicted labels, given the true labels.\n",
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" '''\n",
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" return np.sum(pred_labels == true_labels) / true_labels.shape[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Part 1: Image Compression\n",
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"\n",
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"In this part of the problem set, we shall look at how we can perform lossy compression of images."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.1 Concept - How can K-means be used to compress images?\n",
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"\n",
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"##### Typical Image Representation\n",
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"\n",
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"Before we attempt compressing images, let us have a look at how images used in this problem set are represented digitally. \n",
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"\n",
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"Suppose we have a colour image `I` that has a height of $h$ pixels and width of $w$ pixels, i.e. it contains $h \\times w$ pixels. Then, it can be represented by a 3D array that has the shape $(h, w, 3)$, since each pixel has 3 values -- one for each of the RGB (red, green and blue) channels -- that determine the pixel colour. To be more specific, a pixel located at the `i`th row and `j`th column of image `I` contains `I[i][j][0]`, `I[i][j][1]` and `I[i][j][2]` amount of red, blue and green respectively, where $0 \\leq$ `I[i][j][c]` $\\leq 255$, with $c \\in \\{0, 1, 2\\}$.\n",
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"\n",
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"For example, if the pixel along the 3rd row and 5th column is black, we will have `I[3][5][0]` $=$ `I[3][5][1]` $=$ `I[3][5][2]` $= 0$; and if the pixel along the 2nd row and 1st column is white, we will have `I[2][1][0]` $=$ `I[2][1][1]` $=$ `I[2][1][2]` $= 255$.\n",
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"\n",
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"##### Motivation for Compression\n",
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"\n",
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"Observe that based on the above image representation, to encode the amount of red in one pixel, we need 8 bits (to represent values from 0 to 255), likewise for the green and blue channels. In other words, we will need a total of 24 bits to encode each pixel's colour. \n",
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"\n",
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"However, upon closer inspection, we will notice that we do not in fact need 24 bits for each pixel because the number of distinct colours in a natural image is often much less than $2^{24}$.\n",
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"\n",
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"Moreover, the perceptible difference in colour between pixels with RGB values that only differ slightly is usually minimal. Therefore, we can afford to conflate colours with similar RGB values without a significant loss in visual quality of the image. The example in the figure below illustrates this. \n",
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"\n",
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"<img src=\"images/colour_differences.png\" width=\"300\">\n",
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"\n",
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"##### Overview of Compression Procedure\n",
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"\n",
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"Instead of accommodating all $2^{24}$ colours in the RGB colour space, we shall limit the possible colours to some fixed $k$, where $k \\in \\mathbb{N}$ and $k \\geq 2$.\n",
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"\n",
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"To select these $k$ colours from the $2^{24}$ colours in the RGB colour space, we shall use the _K-Means algorithm_, with the pixel values as input data, to find $k$ _clusters_. Then, the $k$ _centroids_ of these clusters will be the $k$ colours.\n",
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"\n",
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"Next, every pixel $p$ in the $i$-th cluster, where $0 \\leq i < k$, will be recoloured to the its centroid's value. For example, if the zeroth centroid is $[253, 0, 0]$ and $p_0 = [255, 0, 0]$ is assigned to the zeroth cluster, then in the compressed image, $p_0$ will have a value of $[253, 0, 0]$.\n",
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"\n",
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"We can then represent the compressed image `I'` of `I` with a sequence `S` of $k$ colours, and a 2D $h \\times w$ matrix, where $0 \\leq $ `I'[i][j]` $< k$. Then, when rendering the image on screen, the pixel along the `i`th row and `j`th column will have the colour given by `S[I'[i][j]]`.\n",
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"\n",
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"Notice that this proposed encoding of an image can reduce the memory requirements for storing the image because now, each pixel only needs $\\lceil log_2k \\rceil$ bits instead of 24 bits. Alas, the reduction is dependent on our choice of $k$ (since a larger $k$ means that we will incur a greater overhead cost for encoding `S`), and the dimensions of the image."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Example on how K-Means can be used to compress image \n",
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"\n",
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"The images demonstrate the use of K-Means in image compression. The original image is a shaded gradient from red to blue.\n",
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"\n",
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"<img src=\"images/red_blue.jpg\" width=\"300\" height=\"150\">\n",
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"\n",
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"When K-Means is applied with k=30, the image is compressed into 30 different shades of color. Each shade is formed by grouping similar pixel colors together based on their proximity to each other. The result is a compressed image with 30 different color regions instead of the original gradient. The images below show the compressed image when k = 30, k = 10 and k = 2 respectively.\n",
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"\n",
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"\n",
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"<img src=\"images/compressed_red_and_blue_k=30.png\" width=\"300\" height=\"150\">\n",
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"<img src=\"images/compressed_red_and_blue_k=10.png\" width=\"300\" height=\"150\">\n",
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"<img src=\"images/compressed_red_and_blue_k=2.png\" width=\"300\" height=\"150\">\n",
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"\n",
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"In summary, K-Means can be used in image compression by grouping similar pixels together and reducing the overall number of colors in an image. This technique can help to reduce file size and processing time without significantly affecting the visual quality of the image if k is chosen correctly."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Concept - Implementing the K-Means Algorithm\n",
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"\n",
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"We begin by implementing the K-Means algorithm which we will be using for the rest of this problem set. The K-means procedure consists of separate steps, which you are to implement step-by-step.\n",
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"\n",
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"Please note that your solution **should work for any type of data, not only images**; and the only requirement for the efficiency of your implementation of the K-Means algorithm is that it is sufficiently fast to be used throughout this problem set, where appropriate. Lastly, as mentioned earlier, you are **NOT allowed** to import any additional packages.\n",
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"\n",
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"##### Review of the K-Means Algorithm\n",
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"\n",
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"Recall that the general procedure for K-Means clustering is as follows.\n",
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"\n",
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"1. Randomly initialise k centroids, each representing the centroid of a cluster.\n",
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"2. Assign each data point to the closest cluster.\n",
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"3. Based on the new cluster assignment, compute and update the centroid for each cluster.\n",
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"4. Repeat steps 2 and 3 until convergence.\n",
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"\n",
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"**Gentle reminder that there is penalty for using iterative method where numpy is possible. We have written down the number of loops needed in each of the task as a comment.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
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"source": [
|
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"## 1.1: Implementing the K-Means Algorithm"
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|
]
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|
},
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|
{
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"cell_type": "markdown",
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|
"metadata": {},
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"source": [
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"### Task 1.1.1 Assign Points to Centroids\n",
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"\n",
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"Our first task is to implement `assign_clusters`.\n",
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"\n",
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"This function has two parameters `X` and `centroids`. In this case, `X` is an $m \\times n$ matrix, where $m$ is the number of samples and $n$ is the number of features which each sample has; and `centroids` is an `n_clusters` $\\times$ $n$ matrix -- where `n_clusters` is the number of clusters -- such that `centroids[j]` gives the $j$-th cluster's centroid. \n",
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"\n",
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"The purpose of this function is to assign each sample in `X` to the closest cluster. More formally, suppose the $i$-th sample is assigned to the $j$-th cluster, then the Euclidean distance $d_{i, j}$ should be such that $d_{i, j} \\leq d_{i, k}$ $\\forall k \\in \\{0, 1, 2, ..., $ `n_clusters`$-1\\}$. In the event that there exists two clusters that are as close to the $i$-th sample, it should be assigned to the cluster with the smaller index, i.e. if $\\exists$ $j$, $j$' such that $d_{i, j} = d_{i, j'}$, then sample $i$ should be assigned to cluster $s$, where $s = $ min($j$, $j$').\n",
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"\n",
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"This function should then return an array `labels` such that each `labels[i]` is an **integer** indicating which cluster the $i$-th sample has been assigned to."
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.317559Z",
|
|
"start_time": "2024-04-13T11:07:38.305772Z"
|
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}
|
|
},
|
|
"outputs": [],
|
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"source": [
|
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"def assign_clusters(X, centroids):\n",
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" \"\"\"\n",
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" Assigns each sample in X to the closest cluster.\n",
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"\n",
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" Parameters\n",
|
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" ----------\n",
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" X: np.darray\n",
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" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
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" number of features which each sample has. In other words, the `i`th sample\n",
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" is given by `X[i]`.\n",
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" centroids: np.darray\n",
|
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" An `n_clusters * n` matrix where `n_clusters` is the number of clusters\n",
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" and `n` is the number of features which each sample has. In particular, \n",
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" `centroids[j]` represents the `j`th cluster's centroid.\n",
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"\n",
|
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" Returns\n",
|
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" -------\n",
|
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" An `ndarray` of integers that indicates the cluster assignment for each sample.\n",
|
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" Specifically, if `labels` is the `ndarray` returned, then `labels[i]` indicates\n",
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" that the `i`th sample in X has been assigned to the `labels[i]`th cluster, where\n",
|
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" `labels[i]` is a value in the interval [0, `n_clusters`). This cluster should\n",
|
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" be the one with a centroid that is closest to `X[i]` in terms of its Euclidean\n",
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" distance. Note that this array should be an array of integers.\n",
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"\n",
|
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" Note\n",
|
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" ----\n",
|
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" If there are multiple possible closest clusters for the `i`th sample in X,\n",
|
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" assign it to the cluster with the smallest index. For example, if `X[0]` is\n",
|
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" as close to `centroids[0]` as it is to `centroids[1]`, it should be assigned\n",
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" to the 0th cluster instead of the 1st cluster, since 0 < 1.\n",
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" \"\"\"\n",
|
|
" # calculate euclidian distance between each X and centroids\n",
|
|
" labels = np.zeros(X.shape[0], dtype=int)\n",
|
|
" # the following bit of code was written with the assistance of chatGPT. \n",
|
|
" for i,sample in enumerate(X):\n",
|
|
" # Calculate the Euclidean distance between the sample and each centroid\n",
|
|
" # sample - centroids gives a matrix that is each broadcasted such that each sample row is subtracted by the centroids, giving a matrix of shape (n_clusters, n)\n",
|
|
" # print(i, sample, sample-centroids)\n",
|
|
" # Then it calculates the norm of each row of the matrix, which is the euclidean distance between the sample and each centroid. \n",
|
|
" distances = np.linalg.norm(sample - centroids, axis=1)\n",
|
|
" # print(i, distances)\n",
|
|
" \n",
|
|
" # Find the index of the centroid with the minimum distance\n",
|
|
" min_index = np.argmin(distances, axis=0)\n",
|
|
" \n",
|
|
" # Assign the sample to the closest cluster\n",
|
|
" labels[i] = min_index\n",
|
|
" return labels\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 67,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.393751Z",
|
|
"start_time": "2024-04-13T11:07:38.386840Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"X_111 = np.arange(20).reshape((5, 4))\n",
|
|
"labels_111 = assign_clusters(X_111, np.copy(X_111))\n",
|
|
"\n",
|
|
"assert np.issubdtype(labels_111.dtype, int)\n",
|
|
"\n",
|
|
"# Public test case 2\n",
|
|
"assert np.all(labels_111 == np.arange(5))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Testing assignment of points to centroids\n",
|
|
"\n",
|
|
"Use the code below to visualise the effect of running your cluster assignment algorithm on another set of (non-image) data. \n",
|
|
"\n",
|
|
"Note that the centroids of the data are marked by a red \"$+$\", and each unique colour represents a unique grouping of elements.\n",
|
|
"\n",
|
|
"You should expect to see logical groupings of elements based on their distance to the nearest centroid, as specified above."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 68,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.493645Z",
|
|
"start_time": "2024-04-13T11:07:38.422517Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"###############\n",
|
|
"# For testing #\n",
|
|
"###############\n",
|
|
"X_sample = np.array([\n",
|
|
" [0, 0], [0, 1], [1, 0], [1, 1], [2, 1],\n",
|
|
" [7, 8], [8, 8], [8, 9], [9, 8], [9, 9],\n",
|
|
" [0, 8], [1, 8], [0, 9], [1, 9],\n",
|
|
"])\n",
|
|
"centroids = np.array([[1,1],[0,9],[9,9]])\n",
|
|
"labels = assign_clusters(X_sample, centroids)\n",
|
|
"plt.scatter(X_sample[:,0], X_sample[:,1], c=labels)\n",
|
|
"plt.scatter(centroids[:,0], centroids[:,1], marker='+', color='r')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 1.1.2: Update Centroids\n",
|
|
"\n",
|
|
"Now that we have completed implementing step 2 of the K-Means algorithm,\n",
|
|
"let us move on to step 3 by implementing `update_centroids`.\n",
|
|
"\n",
|
|
"Given `X`, `labels` and `n_clusters` as inputs to this function, we need to compute the updated centroids for each cluster. Here, `labels` is such that if the sample point $i$ is assigned to the $j$-th cluster, then `labels[i]` $=$ $j$.\n",
|
|
"\n",
|
|
"This function should return an `n_clusters` $\\times$ $n$ matrix `centroids` such that `centroids[j]` gives the updated centroid of the $j$-th cluster."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 69,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.497758Z",
|
|
"start_time": "2024-04-13T11:07:38.494906Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def update_centroids(X, labels, n_clusters):\n",
|
|
" '''\n",
|
|
" Updates the centroids based on the (new) assignment of clusters.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" X: np.darray\n",
|
|
" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
|
|
" number of features which each sample has. In other words, the `i`th sample\n",
|
|
" is given by `X[i]`.\n",
|
|
" labels: np.darray\n",
|
|
" An array of `m` values, where `m` is the number of samples, that indicates\n",
|
|
" which cluster the samples have been assigned to, i.e. the `i`th\n",
|
|
" sample is assigned to the `labels[i]`th cluster.\n",
|
|
" n_clusters: int\n",
|
|
" No. of clusters.\n",
|
|
"\n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" The `centroids`, an `ndarray` with shape `(n_clusters, n)`, for each cluster,\n",
|
|
" based on the current cluster assignment as specified by `labels`. In particular,\n",
|
|
" `centroids[j]` returns the centroid for the `j`th cluster.\n",
|
|
" '''\n",
|
|
" centroids = np.zeros((n_clusters, X.shape[1]))\n",
|
|
" for i in range(n_clusters):\n",
|
|
" # Find the samples that are assigned to the i-th cluster\n",
|
|
" cluster_samples = X[labels == i]\n",
|
|
" # Calculate the mean of the samples to get the new centroid\n",
|
|
" centroids[i] = np.mean(cluster_samples, axis=0)\n",
|
|
" return centroids"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 70,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.556723Z",
|
|
"start_time": "2024-04-13T11:07:38.553797Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"output_112 = update_centroids(np.array([[1, 2, 3], [1, 2, 3], [5, 2, 100], [1, 2, 3], [5, 2, 100], [5, 2, 100], [1, 2, 3]]), np.array([0, 0, 1, 0, 1, 1, 0]), 2)\n",
|
|
"expected_112 = np.array([[1, 2, 3], [5, 2, 100]])\n",
|
|
"assert np.all(output_112 == expected_112)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Testing updating of centroids\n",
|
|
"\n",
|
|
"Use the code below to visualise the effect of updating existing centroids.\n",
|
|
"\n",
|
|
"The overall assignment of the clusters for each individual point should remain the same, but the location of the centroid should be more \"centralised\" to each logical grouping of points."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.629079Z",
|
|
"start_time": "2024-04-13T11:07:38.569897Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"###############\n",
|
|
"# For testing #\n",
|
|
"#\n",
|
|
"# The image generated shows the position of the old centroids and the new centroids #\n",
|
|
"###############\n",
|
|
"new_centroids = update_centroids(X_sample, labels, len(centroids))\n",
|
|
"plt.scatter(X_sample[:,0], X_sample[:,1], c=labels)\n",
|
|
"plt.scatter(centroids[:,0], centroids[:,1], marker='+', color='r', label='old centroids')\n",
|
|
"plt.scatter(new_centroids[:,0], new_centroids[:,1], marker='x', color='g', label='new centroids')\n",
|
|
"plt.legend(loc=\"lower right\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 1.1.3: Check for Convergence\n",
|
|
"Next, let us implement the function `check_convergence`, which returns true if and only if the convergence conditions are met.\n",
|
|
"\n",
|
|
"Notice that this function has the parameters `prev_centroids` and `centroids`. The difference between `prev_centroids` and `centroids` is that the latter indicates the centroids that have been found in the current iteration while the former indicates those of the previous iteration.\n",
|
|
"\n",
|
|
"In addition, this function also has the `threshold` parameter which determines the convergence criteria, as described in the next paragraph.\n",
|
|
"\n",
|
|
"In this case, convergence is met when for each cluster $j$, the Euclidean distance between its current centroid and previous centroid is __strictly less than__ `threshold`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 72,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.632823Z",
|
|
"start_time": "2024-04-13T11:07:38.630353Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def check_convergence(prev_centroids, centroids, threshold):\n",
|
|
" '''\n",
|
|
" Checks whether the algorithm has converged.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" prev_centroids: np.darray\n",
|
|
" An `n_clusters * n` matrix where `n_clusters` is the number of clusters\n",
|
|
" and `n` is the number of features which each sample has. In particular, \n",
|
|
" `prev_centroids[j]` represents the `j`th cluster's centroid in the\n",
|
|
" PREVIOUS iteration.\n",
|
|
" centroids: np.darray\n",
|
|
" An `n_clusters * n` matrix where `n_clusters` is the number of clusters\n",
|
|
" and `n` is the number of features which each sample has. In particular, \n",
|
|
" `centroids[j]` represents the `j`th cluster's centroid in the CURRENT\n",
|
|
" iteration.\n",
|
|
" threshold: double\n",
|
|
" If each cluster is such that the Euclidean distance between its centroids\n",
|
|
" in the current and previous iteration is strictly less than `threshold`,\n",
|
|
" the algorithm is deemed to have converged.\n",
|
|
"\n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" `True` if and only if the Euclidean distance between each\n",
|
|
" cluster's centroid in the previous and current iteration is strictly\n",
|
|
" less than `threshold`.\n",
|
|
" '''\n",
|
|
" # Calculate the Euclidean distance between the previous and current centroids and return True if all distances less than threshold\n",
|
|
" return np.all(np.linalg.norm(centroids - prev_centroids, axis=1) < threshold)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 73,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.660329Z",
|
|
"start_time": "2024-04-13T11:07:38.657088Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"False\n",
|
|
"False\n",
|
|
"True\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"\n",
|
|
"assert not check_convergence(np.array([[0, 0, 0], [1, 0, 0]]), np.array([[0, 100, 0], [20, 0, 0.009]]), 0.01)\n",
|
|
"\n",
|
|
"# Public test case 2\n",
|
|
"assert check_convergence(np.array([[0, 0, 0], [1, 0, 0]]), np.array([[0, 0.001, 0], [1.0002, 0, 0.009]]), 0.01)\n",
|
|
"\n",
|
|
"###############\n",
|
|
"# For testing #\n",
|
|
"###############\n",
|
|
"print( check_convergence(centroids, new_centroids, .1) ) # False\n",
|
|
"print( check_convergence(centroids, new_centroids, .5) ) # False\n",
|
|
"print( check_convergence(centroids, new_centroids, 10) ) # True"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 1.1.4: Performing K-Means Once\n",
|
|
"Let us put together what we have done so far by implementing the function `k_means_once`!\n",
|
|
"\n",
|
|
"This function takes `X`, `initial_centroids` and `threshold` as arguments. Here, `initial_centroids` is an `n_clusters` $\\times$ $n$ matrix such that `initial_centroids[j]` is the centroid of the initial cluster $j$.\n",
|
|
"\n",
|
|
"It then returns two values, the cluster assignment and the centroid of each cluster, respectively. In other words, we expect\n",
|
|
"\n",
|
|
"`labels, centroids = k_means_once(X, initial_centroids, threshold)`\n",
|
|
"\n",
|
|
"where `labels[i]`, for $0 \\leq i < m$, is the cluster assignment for the $i$-th sample in `X`, and `centroids[j]` is the centroid of the $j$-th cluster, after K-Means clustering is done."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 74,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.690053Z",
|
|
"start_time": "2024-04-13T11:07:38.687106Z"
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|
}
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|
},
|
|
"outputs": [],
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"source": [
|
|
"def k_means_once(X, initial_centroids, threshold):\n",
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|
" '''\n",
|
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" Assigns each point in X to a cluster by running the K-Means algorithm\n",
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|
" once till convergence.\n",
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"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" X: np.darray\n",
|
|
" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
|
|
" number of features which each sample has. In other words, the `i`th sample\n",
|
|
" is given by `X[i]`.\n",
|
|
" initial_centroids: np.darray\n",
|
|
" An `n_clusters * n` matrix, where `n_clusters` is the number of clusters and\n",
|
|
" `n` is the number of features that each sample in X has. This matrix is such\n",
|
|
" that the `i`th row represents the initial centroid of the `i`th cluster.\n",
|
|
" threshold: double\n",
|
|
" During the clustering process, if the difference in centroids between\n",
|
|
" two consecutive iterations is less than `threshold`, the algorithm is\n",
|
|
" deemed to have converged.\n",
|
|
"\n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" The cluster assignment for each sample, and the `n_clusters` centroids found. \n",
|
|
" In particular, the cluster assignment for the ith sample in `X` is given by `labels[i]`,\n",
|
|
" where 0 <= `labels[i]` < `n_clusters`. Moreover, suppose c = `labels[i]`. Then,\n",
|
|
" the `i`th sample belongs to the cluster c with the centroid given by `centroids[c]`.\n",
|
|
" '''\n",
|
|
" prev_centroid, centroids = np.zeros(initial_centroids.shape), initial_centroids\n",
|
|
" while not check_convergence(prev_centroid, centroids, threshold):\n",
|
|
" prev_centroid = centroids\n",
|
|
" labels = assign_clusters(X, centroids)\n",
|
|
" centroids = update_centroids(X, labels, initial_centroids.shape[0])\n",
|
|
" return labels, centroids"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 75,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.787262Z",
|
|
"start_time": "2024-04-13T11:07:38.770996Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"intial_centroids_114_1 = np.array([[1, 0, 2], [-100, 200, 300]])\n",
|
|
"X_114_1 = np.array([ [-9.72926939e+01, 2.01498559e+02, 3.02113436e+02], [-9.98584016e+01, 2.00621416e+02, 3.03353122e+02], [-9.67640524e+01, 2.03076926e+02, 3.01918388e+02], [ 5.98604968e+00, 4.90417669e+00, 5.42770992e+00], [ 4.25229638e+00, 3.44223365e+00, 3.94460712e+00],[ 1.67548253e+00, 3.60744170e+00, 4.62677161e+00], [ 2.55120938e+00, 2.42917679e+00, 6.44743917e+00], [ 5.67021758e+00, 1.78897598e+00, 4.85764915e+00], [ 2.60934696e+00, 2.97150015e+00, 3.68955613e+00], [-9.80419050e+01, 2.04451372e+02, 3.01135788e+02], [-9.68840643e+01, 2.00420077e+02, 3.04163221e+02], [-9.60645085e+01, 2.01196847e+02, 3.04382421e+02], [-9.97071598e+01, 2.01680585e+02, 3.00751397e+02], [-9.77483032e+01, 2.03981621e+02, 3.01153211e+02], [-9.97398935e+01, 2.02022759e+02, 3.00992565e+02], [-9.95462348e+01, 2.02901662e+02, 3.01493481e+02], [-9.66400256e+01, 2.00997577e+02, 3.04710566e+02], [-9.81744492e+01, 2.00527476e+02, 3.03145541e+02], [-9.53642272e+01, 2.02201886e+02, 3.04772952e+02], [-9.75005209e+01, 2.02126143e+02, 3.03101067e+02], [ 5.97548253e+00, 4.74471837e+00, 4.30022570e+00], [-9.62113558e+01, 2.02487113e+02, 3.02646561e+02], [-9.60710715e+01, 2.02073279e+02, 3.03672418e+02], [ 4.55571439e+00, 4.66029843e+00, 2.57466317e+00], [ 4.64507559e+00, 4.63711964e+00, 6.83963095e+00], [-9.99264685e+01, 2.04318200e+02, 3.04905975e+02], [-9.52139491e+01, 2.00743820e+02, 3.04863144e+02], [ 5.44967778e+00, 4.11186914e+00, 4.39993962e+00], [-9.88381354e+01, 2.04009403e+02, 3.04617651e+02],[-9.86693486e+01, 2.02694672e+02, 3.02213764e+02], [-9.53449134e+01, 2.00202554e+02, 3.03660031e+02], [4.07186623e+00, 1.41826826e-01, 5.59609886e+00], [1.07995865e+00, 3.78975501e+00, 4.56379362e+00]])\n",
|
|
"output_114_1, _ = k_means_once(X_114_1, intial_centroids_114_1, 0.1)\n",
|
|
"expected_114_1 = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0])\n",
|
|
"\n",
|
|
"assert np.all(output_114_1 == expected_114_1)\n",
|
|
"\n",
|
|
"# Public test case 2\n",
|
|
"intial_centroids_114_2 = np.array([[1, 0, 2, 32, 4], [-100, 200, 300, 150, -128], [1000, 150, -20, 500, -10]])\n",
|
|
"X_114_2 = np.array([[ 1.56491720e+00, 7.37381262e-01, 6.74026762e+00, 3.61723171e+01, 7.12403246e+00], [ 3.99284910e+00, 2.84387038e-01, 2.25724207e+00, 3.44348858e+01, 5.48927846e+00], [-9.69068153e+01, 2.04992404e+02, 3.01872102e+02, 1.51655272e+02, -1.27365154e+02], [-9.71911561e+01, 2.03214951e+02, 3.02305517e+02, 1.50122215e+02, -1.24891304e+02], [-9.84892927e+01, 2.04022064e+02, 3.00511482e+02, 1.50949813e+02, -1.23514390e+02], [-9.91883972e+01, 2.03602966e+02, 3.02670367e+02, 1.53879734e+02, -1.25370461e+02], [ 2.15315930e+00, 2.33996767e+00, 2.03527496e+00, 3.32534705e+01, 8.63696144e+00], [ 1.00452684e+03, 1.51308844e+02,-1.91088647e+01, 5.03463870e+02, -6.92172061e+00], [ 1.00085330e+03, 1.53669237e+02,-1.56212784e+01, 5.00843984e+02, -5.50511348e+00], [ 1.51751682e+00, 5.56468924e-01, 2.81213650e+00, 3.32390654e+01, 8.50429903e+00], [ 1.82309225e+00, 4.90897221e-01, 5.53413182e+00, 3.22724318e+01, 5.49885178e+00], [-9.84314061e+01, 2.01608694e+02, 3.04407217e+02, 1.51907009e+02, -1.23924059e+02], [ 2.92258291e+00, 1.04948810e+00, 3.18654930e+00, 3.22594379e+01, 6.51531796e+00], [-9.97080699e+01, 2.00483592e+02, 3.02897705e+02, 1.54860298e+02, -1.24697593e+02], [ 1.00295696e+03, 1.51894953e+02,-1.55318935e+01, 5.01907666e+02, -9.58991427e+00], [-9.72025657e+01, 2.04930408e+02, 3.01296162e+02, 1.54463456e+02, -1.25405463e+02], [-9.98900358e+01, 2.01951120e+02, 3.03069414e+02, 1.52185855e+02, -1.23697303e+02], [ 1.00485325e+03, 1.50588049e+02,-1.61681193e+01, 5.00208238e+02, -5.19900895e+00], [ 1.00162638e+03, 1.54835658e+02,-1.78113123e+01, 5.03155981e+02, -5.41095710e+00], [-9.99262989e+01, 2.01734123e+02, 3.04372332e+02, 1.51584896e+02, -1.26046111e+02], [ 1.00268952e+03, 1.51464525e+02,-1.87672043e+01, 5.03205192e+02, -7.01124970e+00], [ 1.00304660e+03, 1.54898230e+02,-1.94315847e+01, 5.02473927e+02, -9.07506503e+00], [ 5.72555017e+00, 2.21871100e+00, 5.60023390e+00, 3.57522016e+01, 5.42109897e+00], [ 1.00201639e+03, 1.52022011e+02,-1.58161709e+01, 5.01134304e+02, -8.99219657e+00], [-9.59006093e+01, 2.00043326e+02, 3.02233922e+02, 1.53674545e+02, -1.24757694e+02], [-9.73563260e+01, 2.02452060e+02, 3.03398007e+02, 1.51560495e+02, -1.25232723e+02], [-9.52311306e+01, 2.04100698e+02, 3.00158417e+02, 1.52744470e+02, -1.24073020e+02], [ 1.00209939e+03, 1.54618209e+02,-1.77651413e+01, 5.02467660e+02, -6.78368846e+00], [ 1.00350617e+03, 1.53013154e+02,-1.85411405e+01, 5.01362388e+02, -6.06756023e+00], [-9.68708151e+01, 2.04948821e+02, 3.01200395e+02, 1.52091351e+02, -1.24405273e+02], [ 1.00426189e+03, 1.52252591e+02,-1.66751292e+01, 5.04040696e+02, -6.93514438e+00], [ 1.00056503e+03, 1.52710502e+02,-1.75870621e+01, 5.04588456e+02, -7.64625156e+00], [ 1.00434411e+03, 1.53118866e+02,-1.75201216e+01, 5.01171910e+02, -6.41971954e+00], [ 1.00447310e+03, 1.54898691e+02,-1.80266001e+01, 5.01731525e+02, -8.49464067e+00], [ 1.17401540e+00, 2.00969872e+00, 4.61992013e+00, 3.45662014e+01, 8.73557856e+00], [ 1.00398066e+03, 1.53452624e+02,-1.92830333e+01, 5.02119668e+02, -9.93636756e+00], [ 1.00243798e+03, 1.51927175e+02,-1.59887628e+01, 5.02110072e+02, -8.55139135e+00], [-9.78056355e+01, 2.00998191e+02, 3.03530065e+02, 1.53873608e+02, -1.26466762e+02], [ 1.00103397e+03, 1.52399510e+02,-1.67024619e+01, 5.02323216e+02, -8.32458928e+00], [-9.69470516e+01, 2.04320439e+02, 3.01198535e+02, 1.53765658e+02, -1.27862417e+02], [-9.97896159e+01, 2.02641681e+02, 3.00014716e+02, 1.53307670e+02, -1.26516157e+02], [ 1.00290761e+03, 1.53036753e+02,-1.63533704e+01, 5.03202986e+02, -9.34091385e+00], [ 1.00254891e+03, 1.54517630e+02,-1.74044743e+01, 5.03481636e+02, -6.31593142e+00], [-9.89251000e+01, 2.02110923e+02, 3.00125395e+02, 1.54144876e+02, -1.24536347e+02], [ 1.48246410e+00, 2.31907552e+00, 5.10935695e+00, 3.56577529e+01, 8.42158349e+00], [ 2.39698975e+00, 3.68390714e+00, 6.51639124e+00, 3.30049071e+01, 5.18650548e+00], [-9.55947307e+01, 2.04884666e+02, 3.02226645e+02, 1.51312246e+02, -1.27759747e+02], [ 1.00363194e+03, 1.53067050e+02,-1.60493725e+01, 5.00660091e+02, -5.99451690e+00], [-9.61151926e+01, 2.03094310e+02, 3.00117847e+02, 1.53942660e+02, -1.25748154e+02], [ 4.87809265e+00, 3.91657243e+00, 6.29320750e+00, 3.65373345e+01, 6.83891257e+00], [ 1.00330495e+03, 1.52184344e+02,-1.53334017e+01, 5.04613027e+02, -7.97179155e+00], [ 1.00376830e+03, 1.51041189e+02,-1.59458846e+01, 5.01900942e+02, -9.64050948e+00], [ 1.00177926e+03, 1.53088579e+02,-1.92474048e+01, 5.02769755e+02, -6.67230911e+00], [ 5.42322619e+00, 3.29985315e+00, 3.84925530e+00, 3.56765310e+01, 8.12679837e+00], [-9.87394670e+01, 2.04224169e+02, 3.03990890e+02, 1.53551472e+02, -1.23338799e+02]])\n",
|
|
"output_114_2, _ = k_means_once(X_114_2, intial_centroids_114_2, 0.1)\n",
|
|
"expected_114_2 = np.array([0, 0, 1, 1, 1, 1, 0, 2, 2, 0, 0, 1, 0, 1, 2, 1, 1, 2, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 0, 2, 2, 1, 2, 1, 1, 2, 2, 1, 0, 0, 1, 2, 1, 0, 2, 2, 2, 0, 1,])\n",
|
|
"\n",
|
|
"assert np.all(output_114_2 == expected_114_2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.877056Z",
|
|
"start_time": "2024-04-13T11:07:38.823121Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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BAACMoVgAAABjKBYAAMAYigUAADCGYgEAAIyhWAAAAGMoFgAAwBiKBQAAMCbkYvH111/r9ttvV3JyshISEtSzZ0/t2LGjJrIBAIAo0yCUlU+ePKkBAwboyiuv1DvvvKOUlBQdOnRITZo0qaF4AAAgmoRULObNmye3260lS5YUL0tNTTWdCQAARKmQToWsXbtW6enpuuWWW5SSkqJevXpp8eLF5T7G5/PJ6/UG3QAAQN0UUrE4fPiwFi1apI4dO+q9997TxIkTdffdd+uPf/xjmY/JyMiQy+Uqvrnd7mqHBgAAtZNl27Zd2ZVjYmKUnp6urVu3Fi+7++67tX37dn388celPsbn88nn8xX/7PV65Xa75fF4lJSUVI3oAAAgXLxer1wuV4X775COWLRu3Vpdu3YNWtalSxdlZWWV+ZjY2FglJSUF3QAAQN0UUrEYMGCA9u/fH7TswIEDat++vdFQAAAgOoVULKZPn65t27bp8ccf1z//+U+tWLFCmZmZmjRpUk3lAwAAUSSkYtG3b1+tWbNGf/rTn9S9e3fNnTtXCxYs0JgxY2oqHwAAiCIhDd40obKDPwAAQO1RI4M3AQAAykOxAAAAxlAsAACAMRQLAABgDMUCAAAYQ7EAAADGUCwAAIAxFAsAAGAMxQIAABhDsQAAAMZQLAAAgDEUCwAAYAzFAgAAGEOxAAAAxlAsAACAMRQLAABgDMUCAAAYQ7EAAADGUCwAAIAxFAsAAGAMxQIAABhDsQAAAMZQLAAAgDEUCwAAYAzFAgAAGEOxAAAAxlAsAACAMRQLAABgDMUCAAAYQ7EAAADGUCwAAIAxFAsAAGAMxQIAABhDsQAAAMZQLAAAgDEUCwAAYAzFAgAAGEOxAAAAxlAsAACAMRQLAABgDMUCAAAYQ7EAAADGUCwAAIAxFAsAAGAMxQIAABhDsQAAAMZQLAAAgDEUCwAAYAzFAgAAGEOxAAAAxlAsAACAMRQLAABgDMUCAAAYQ7EAAADGUCwAAIAxFAsAAGAMxQIAABhDsQAAAMZQLAAAgDEUCwAAYAzFAgAAGEOxAAAAxlAsAACAMRQLAABgTLWKRUZGhizL0rRp0wzFAQAA0azKxWL79u3KzMxUjx49TOYBAABRrErF4tSpUxozZowWL16spk2bms4EAACiVJWKxaRJk3Tddddp6NChFa7r8/nk9XqDbgAAoG5qEOoDVq5cqZ07d2r79u2VWj8jI0O//vWvQw4GAACiT0hHLLKzszV16lS99NJLiouLq9RjZs6cKY/HU3zLzs6uUlAAAFD7WbZt25Vd+Y033tDIkSPldDqLl/n9flmWJYfDIZ/PF3Rfabxer1wulzwej5KSkqqeHAAAhE1l998hnQq56qqrtGfPnqBlP/3pT9W5c2fdd999FZYKAABQt4VULBITE9W9e/egZY0aNVJycnKJ5QAAoP5h5k0AAGBMyN8K+b4NGzYYiAEAAOoCjlgAAABjKBYAAMAYigUAADCGYgEAAIyhWAAAAGMoFgAAwBiKBQAAMIZiAQAAjKFYAAAAYygWAADAGIoFAAAwhmIBAACMoVgAAABjKBYAAMAYigUAADCGYgEAAIyhWAAAAGMoFgAAwBiKBQAAMIZiAQAAjKFYAAAAYygWAADAGIoFAAAwhmIBAACMoVgAAABjKBYAAMAYigUAADCGYgEAAIyhWAAAAGMoFgAAwBiKBQAAMIZiAQAAjKFYAAAAYygWAADAGIoFAAAwhmIBAACMoVgAAABjKBYAAMAYigUAADCGYgEAAIyhWAAAAGMoFgAAwBiKBQAAMIZiAQAAjKFYAAAAYygWAADAGIoFAAAwhmIBAACMoVgAAABjKBYAAMAYigUAADCGYgEAAIyhWAAAAGMoFgAAwBiKBQAAMIZiAQAAjKFYAAAAYygWAADAGIoFAAAwhmIBAACMoVgAAABjKBYAAMAYigUAADCGYgEAAIwJqVhkZGSob9++SkxMVEpKikaMGKH9+/fXVDYAABBlQioWGzdu1KRJk7Rt2zatW7dOhYWFGjZsmE6fPl1T+QAAQBSxbNu2q/rg48ePKyUlRRs3btSgQYMq9Riv1yuXyyWPx6OkpKSqvjQAAAijyu6/G1TnRTwejySpWbNmZa7j8/nk8/mCggEAgLqpyoM3bdvWjBkzNHDgQHXv3r3M9TIyMuRyuYpvbre7qi8JAABquSqfCpk0aZL+8pe/aPPmzWrbtm2Z65V2xMLtdnMqBACAKFKjp0KmTJmitWvXatOmTeWWCkmKjY1VbGxsVV4GAABEmZCKhW3bmjJlitasWaMNGzaoQ4cONZULAABEoZCKxaRJk7RixQq9+eabSkxM1LFjxyRJLpdL8fHxNRIQAABEj5DGWFiWVeryJUuWaPz48ZV6Dr5uCgBA9KmRMRbVmPICAADUA1wrBAAAGEOxAAAAxlAsAACAMRQLAABgDMUCAAAYQ7EAAADGUCwAAIAxFAsAAGAMxQIAABhDsQAAAMZQLAAAgDEUCwAAYAzFAgAAGEOxAAAAxlAsAACAMRQLAABgDMUCAAAYQ7EAAADGUCwAAIAxFAsAAGAMxQIAABhDsQAAAMZQLAAAgDEUCwAAYAzFAgAAGNMg0gGq6+wZnzas3KJP3tmpAl+hOvW5WMMn/FDNL0qOdLSQFOQXaMuaT7TljU+Ud+qsUru5de2dQ9Xm4laRjhYSv9+vT97epY2vbFXuydO66JJWGj7hKnXo3i7S0QAAYWDZtm2H8wW9Xq9cLpc8Ho+SkpKq9VxffZ6tX109V99+c1KWw5IdsOVwOGQ5pF/+YZKG3j7IUOqalZN9Qr8a+oi+PviNHA5LgYAth9OhQCCgiU+O003Tr490xErxfpurB4Y/pv3bDxXl9wfkaOBQoDCgW++9QRP+Z4wsy4p0TABAFVR2/x21p0LOnvHpV1fP1Xc5HkmSHSjqR4FAQP7CgOaPW6jPtx2IZMRKCQQCeuDax/XNF/869/O57fAHJFt64Z5l2vrm9khGrLRHRz2jgzu/kHQuv6RAYdE/X3niTf35d+silg0AEB5RWyw2rNyib785WbwD+z6H09JrT70V5lSh2/H+p/pqb3bxDvj7HA5LK/9nTZhThe7Qp19q1wd7ynw/JGnl/6xRIFD2/QCA6Be1xeKTd3bKcpR9WN1fGNC2v+wIY6Kq+d+/7JSzobPM+wMBW/v+96BOe06HMVXoPnl7lxzO8j9OOVknlL3/aJgSAQAiIWqLRf7ZguLTH2Xx5xcqzENIQlaYX1ip9QoquV6kFOYXVmr8RGW3FwAQnaK2WFyafkm5fyFbDksX9+pQ6wcLdkq/WP5Cf7nrNL+omZKSE8OUqGo69kmrcDviGsWqzSXR9S0XAEBoorZYDJ/wQ5XXGeyArZF3Xxu+QFV05egBim8cX2YBshyWbpg8XA5H7X6r+g7vqRbuZDmcpW+Hw+nQNXf8UPGN4sKcDAAQTrV7b1WO5hcl657f/0KWZcnZ4N+bcX7cxdCxg3TVmCsiFa/S4hvHa/aq6XI2dAZvhyVZlqXeQ3vopunXRTBh5TidTj306j2KjY8N2g7p3NGjy1P100dHRygdACBconoeC0n6/OP9evWpt/S/f9khf4FfF/dM1ci7r9NVt19R6//Kv9Dhv3+l155+S5te26aCs/lqe2kb3TBpuK698yo1aBg985gdPXRMrz31ltav+Eh5p86qZbsWun7iMN0w+RrFJcRGOh4AoIoqu/+O+mJxIdu2a/2YispgOwAAtU2dnyCrNHVlJ8Z2AACiVZ0qFgAAILIoFgAAwBiKBQAAMIZiUYM8Zz064j1S6n1HvEfkOesJcyIAAGoWxaKGeM56dM3L12jw0sHK9mQH3ZftydbgpYN1zcvXUC4AAHUKxaKG5ObnKud0jg6fPKwhy4YUl4tsT7aGLBuiwycPK+d0jnLzcyOaEwAAkygWNaRtUlttGLdBaU3TisvF1uytxaUirWmaNozboLZJbSMdFQAAY+rUBFm10YVHKM47XyrcLnfkggEAEIJ6OUFWbeR2ubV85PKgZctHLqdUAADqJIpFDcv2ZGvsmrFBy8auGVtiQCcAAHUBxaIGXXgaJK1pmrbcsSVozAXlAgBQ11AsasgR75ESAzX7u/uXGNBZ1jwXAABEI4pFDUmMSVRKo5QSAzXdLndxuUhplKLEmMQIJwUAwBy+FVKDPGc9ys3PLfUrpUe8R5QYkyhXnCsCyQAACE1l998Nwpip3nHFucosDsxfAcAkv9+vgoKCSMdAFGvYsKGcTme1n4diAQBRzLZtHTt2TN99912ko6AOaNKkiVq1aiXLsqr8HBQLAIhi50tFSkqKEhISqrVDQP1l27bOnDmjnJwcSVLr1q2r/FwUCwCIUn6/v7hUJCcnRzoOolx8fLwkKScnRykpKVU+LcK3QgAgSp0fU5GQkBDhJKgrzn+WqjNeh2IBAFGO0x8wxcRniWIBAACMoVgAAMLKtm3dddddatasmSzL0u7duzVkyBBNmzbN6OvMmTNHPXv2NPqckZaamqoFCxaUu45lWXrjjTfCkqc0DN4EAITVu+++q6VLl2rDhg1KS0tT8+bN9frrr6thw4aRjmbc0qVLNW3aNGNfB96+fbsaNWpk5LlqCsUCAOqpSM0OfOjQIbVu3Vr9+/cvXtasWTPjrxNN8vPzFRMTU+F6LVq0CEOa6uFUCADUQ56zHl3z8jUavHRwiSstZ3uyNXjpYF3z8jXynPUYfd3x48drypQpysrKkmVZSk1NlaQSp0JSU1P1+OOP64477lBiYqLatWunzMzMoOe677771KlTJyUkJCgtLU2zZ88O+dsMe/fu1XXXXaekpCQlJibqiiuu0KFDh4rvX7Jkibp06aK4uDh17txZzz//fPF9X375pSzL0uuvv64rr7xSCQkJuvzyy/Xxxx9LkjZs2KCf/vSn8ng8sixLlmVpzpw5xdv36KOPavz48XK5XLrzzjslSatXr1a3bt0UGxur1NRUPfXUU0F5v38q5ODBgxo0aJDi4uLUtWtXrVu3Lmj9/Px8TZ48Wa1bt1ZcXJxSU1OVkZER0n+jUNWJIxbfHfdo94efqSC/UJ36pKl9V3ekI1XJac9p7fxgj86e9ql9t7bq1OfiSEcCUEfl5ucq53RO8ZWWz18sMduTXXxl5vPrmTxq8eyzz+riiy9WZmamtm/fXu5cCU899ZTmzp2rBx54QK+99pp+/vOfa9CgQercubMkKTExUUuXLlWbNm20Z88e3XnnnUpMTNSvfvWrSmX5+uuvNWjQIA0ZMkQffvihkpKStGXLFhUWFkqSFi9erIcfflgLFy5Ur169tGvXLt15551q1KiRxo0bV/w8s2bN0pNPPqmOHTtq1qxZGj16tP75z3+qf//+WrBggR566CHt379fktS4cePixz3xxBOaPXu2HnzwQUnSjh07dOutt2rOnDm67bbbtHXrVv3iF79QcnKyxo8fXyJ/IBDQjTfeqObNm2vbtm3yer0lxqn85je/0dq1a/XKK6+oXbt2ys7OVnZ2donnMsqugueee85OTU21Y2Nj7d69e9ubNm2q9GM9Ho8tyfZ4PFV56SC+PJ+94Oe/s3/U8DZ7qHVz8W36oNn20cPHqv384VJYWGi/OPNl+9r40UHb8d+9fmkf+vTLSMcDUEvl5eXZn3/+uZ2Xl1elx2d9l2WnPZtma47stGfT7C1ZW4J+zvouy3DiIs8884zdvn37oGWDBw+2p06dWvxz+/bt7dtvv73450AgYKekpNiLFi0q83nnz59v9+nTp/jnhx9+2L788svLXH/mzJl2hw4d7Pz8/FLvd7vd9ooVK4KWzZ071+7Xr59t27b9xRdf2JLsF198sfj+vXv32pLsffv22bZt20uWLLFdLleJ527fvr09YsSIoGU//vGP7auvvjpo2b333mt37do16HHPPPOMbdu2/d5779lOp9POzs4uvv+dd96xJdlr1qyxbdu2p0yZYv/whz+0A4FAmf8dLlTeZ6qy+++QT4WsWrVK06ZN06xZs7Rr1y5dccUVGj58uLKysgxXnvLZtq25tz2tv2R+IH+hP+i+vVv3a9qAB3XyX9+FNVNVLZzyB62ct0b5Z4MP4X2xJ0vTB83WkYPfRCgZgLrM7XJrw7gNSmuapsMnD2vAHwbo8MnDSmuaVnwEI5J69OhR/O+WZalVq1bFU05L0muvvaaBAweqVatWaty4sWbPnh3Svmj37t264oorSh00evz4cWVnZ+tnP/uZGjduXHx79NFHg06VfD/n+amwL8xZlvT09KCf9+3bpwEDBgQtGzBggA4ePCi/P3g/d379du3aqW3bf4+R6devX9A648eP1+7du3XppZfq7rvv1vvvv19hruoKuVg8/fTT+tnPfqYJEyaoS5cuWrBggdxutxYtWlQT+cq056N92vbWDtmBkld9D/gD+u64V68/+3ZYM1XFkQNH9ecX3pdKuXh9wB+Q74xPKx5bHf5gAOoFt8ut5SOXBy1bPnJ5xEuFpBI7fMuyFAgEJEnbtm3TqFGjNHz4cP35z3/Wrl27NGvWLOXn51f6+c9PYV2a86+zePFi7d69u/j22Wefadu2bWXmPD/B1PnHl+f73+6wbbvEBFW2XcrOoZz7vv/43r1764svvtDcuXOVl5enW2+9VTfffHOF2aojpGKRn5+vHTt2aNiwYUHLhw0bpq1bt5b6GJ/PJ6/XG3QzYd2yDXI2KDt+wB/Qu39Yb+S1atIHyzfJUc52+AsD+uufNiv/bOX/ZwGAysr2ZGvsmrFBy8auGVtiQGdts2XLFrVv316zZs1Senq6OnbsqK+++iqk5+jRo4c++uijUgd8tmzZUhdddJEOHz6sSy65JOjWoUOHSr9GTExMqUcbStO1a1dt3rw5aNnWrVvVqVOnUseidO3aVVlZWTp69GjxsvMDRy+UlJSk2267TYsXL9aqVau0evVqffvtt5XehlCFVCxOnDghv9+vli1bBi1v2bKljh07VupjMjIy5HK5im9ut5kW/O2x7+QvLL8Rek7kltv2aoNvj30nS+VPoVpY4Ndpz5kwJQJQX1w4UDOtaZq23LGl+LTIkGVDanW5uOSSS5SVlaWVK1fq0KFD+s1vfqM1a9aE9ByTJ0+W1+vVqFGj9Le//U0HDx7U8uXLiwdazpkzRxkZGXr22Wd14MAB7dmzR0uWLNHTTz9d6ddITU3VqVOntH79ep04cUJnzpT9u/yee+7R+vXrNXfuXB04cEDLli3TwoUL9ctf/rLU9YcOHapLL71UP/nJT/Tpp5/qo48+0qxZs4LWeeaZZ7Ry5Ur94x//0IEDB/Tqq6+qVatWatKkSaW3IVRV+rppaYdqyppffObMmfJ4PMU3U6NRm1/UrNwjFpLUJMVV6+fQb35RswrLT8PYBmrUpHZPiAIguhzxHgkqFRvGbVB/d/+gMRdDlg3REe+RSEct1Q033KDp06dr8uTJ6tmzp7Zu3arZs2eH9BzJycn68MMPderUKQ0ePFh9+vTR4sWLi09tTJgwQS+++KKWLl2qyy67TIMHD9bSpUtDOmLRv39/TZw4UbfddptatGih+fPnl7lu79699corr2jlypXq3r27HnroIT3yyCOlfiNEkhwOh9asWSOfz6f/+I//0IQJE/TYY48FrdO4cWPNmzdP6enp6tu3r7788ku9/fbbcjhqbrYJyw7hT/r8/HwlJCTo1Vdf1ciRI4uXT506Vbt379bGjRsrfA6v1yuXyyWPx6OkpKSqpda5AZoDHyzzfofTodEzR2r8I6Oq/BrhcPTQMY3rNKXUMRaS5Gzg0NU/GaJ7Xvx5eIMBqPXOnj2rL774Qh06dFBcXFxIjz0/j0XO6ZwSAzXPH8lIaZSid8e8WyOTZKF2Ku8zVdn9d0iVJSYmRn369CkxAce6deuCZlALh679Omnwrf1LPSLhaOBQ84uaaeTd14Y1U1W0ubiVbpp2fan3OZwOJSTGa8yDN4U5FYC6zhXn0rtj3tXG8RtLDNR0u9zaOH4jpQJVEvIEWTNmzNDYsWOVnp6ufv36KTMzU1lZWZo4cWJN5CuTZVm6f/kUtWzXXG8+9658eecGN1pS+tWXa/riiXI1r/oRkXC664mxSkpO1Kr5b+iMN694ebf+l2p65n+rVWpKBNMBqKtcca4yi0Np03wDlRHSqZDznn/+ec2fP1/ffPONunfvrmeeeUaDBg2q1GNNnQq50GnvGX320T4V5Bfqkl4donZH7Mvz6e+b9sl3xqf2XdvKfelFkY4EoBarzqkQoDQmToVUqVhUR00UCwCojygWMC3sYywAAADKQ7EAgChXmVkegcow8VmqE1c3BYD6KCYmRg6HQ0ePHlWLFi0UExNT6+fuQe1k27by8/N1/PhxORwOxcTEVPm5KBYAEKUcDoc6dOigb775JmhaZ6CqEhIS1K5du2pNoEWxAIAoFhMTo3bt2qmwsLDS16QASuN0OtWgQYNqH/WiWABAlLMsSw0bNiz18t9AuDF4EwAAGEOxAAAAxlAsAACAMWEfY3F+ok+v1xvulwYAAFV0fr9d0YTdYS8Wubm5kiS3213BmgAAoLbJzc2Vy1X2VW/Dfq2QQCCgo0ePKjEx0ehELl6vV263W9nZ2VyDpBbg/ah9eE9qF96P2oX3o2K2bSs3N1dt2rQpd56LsB+xcDgcatu25i7Hm5SUxIeiFuH9qH14T2oX3o/ahfejfOUdqTiPwZsAAMAYigUAADCmzhSL2NhYPfzww4qNjY10FIj3ozbiPaldeD9qF94Pc8I+eBMAANRddeaIBQAAiDyKBQAAMIZiAQAAjKFYAAAAY+pMsXj++efVoUMHxcXFqU+fPvroo48iHaleysjIUN++fZWYmKiUlBSNGDFC+/fvj3QsnJORkSHLsjRt2rRIR6m3vv76a91+++1KTk5WQkKCevbsqR07dkQ6Vr1VWFioBx98UB06dFB8fLzS0tL0yCOPKBAIRDpa1KoTxWLVqlWaNm2aZs2apV27dumKK67Q8OHDlZWVFelo9c7GjRs1adIkbdu2TevWrVNhYaGGDRum06dPRzpavbd9+3ZlZmaqR48ekY5Sb508eVIDBgxQw4YN9c477+jzzz/XU089pSZNmkQ6Wr01b948vfDCC1q4cKH27dun+fPn64knntBvf/vbSEeLWnXi66Y/+MEP1Lt3by1atKh4WZcuXTRixAhlZGREMBmOHz+ulJQUbdy4UYMGDYp0nHrr1KlT6t27t55//nk9+uij6tmzpxYsWBDpWPXO/fffry1btnBEtRa5/vrr1bJlS/3+978vXnbTTTcpISFBy5cvj2Cy6BX1Ryzy8/O1Y8cODRs2LGj5sGHDtHXr1gilwnkej0eS1KxZswgnqd8mTZqk6667TkOHDo10lHpt7dq1Sk9P1y233KKUlBT16tVLixcvjnSsem3gwIFav369Dhw4IEn69NNPtXnzZl177bURTha9wn4RMtNOnDghv9+vli1bBi1v2bKljh07FqFUkIquhDdjxgwNHDhQ3bt3j3ScemvlypXauXOntm/fHuko9d7hw4e1aNEizZgxQw888IA++eQT3X333YqNjdVPfvKTSMerl+677z55PB517txZTqdTfr9fjz32mEaPHh3paFEr6ovFed+/BLtt20Yvy47QTZ48WX//+9+1efPmSEept7KzszV16lS9//77iouLi3Scei8QCCg9PV2PP/64JKlXr17au3evFi1aRLGIkFWrVumll17SihUr1K1bN+3evVvTpk1TmzZtNG7cuEjHi0pRXyyaN28up9NZ4uhETk5OiaMYCJ8pU6Zo7dq12rRpk9q2bRvpOPXWjh07lJOToz59+hQv8/v92rRpkxYuXCifzyen0xnBhPVL69at1bVr16BlXbp00erVqyOUCPfee6/uv/9+jRo1SpJ02WWX6auvvlJGRgbFooqifoxFTEyM+vTpo3Xr1gUtX7dunfr37x+hVPWXbduaPHmyXn/9dX344Yfq0KFDpCPVa1dddZX27Nmj3bt3F9/S09M1ZswY7d69m1IRZgMGDCjx9esDBw6offv2EUqEM2fOyOEI3hU6nU6+bloNUX/EQpJmzJihsWPHKj09Xf369VNmZqaysrI0ceLESEerdyZNmqQVK1bozTffVGJiYvGRJJfLpfj4+Ainq38SExNLjG9p1KiRkpOTGfcSAdOnT1f//v31+OOP69Zbb9Unn3yizMxMZWZmRjpavfVf//Vfeuyxx9SuXTt169ZNu3bt0tNPP6077rgj0tGil11HPPfcc3b79u3tmJgYu3fv3vbGjRsjHaleklTqbcmSJZGOhnMGDx5sT506NdIx6q233nrL7t69ux0bG2t37tzZzszMjHSkes3r9dpTp06127VrZ8fFxdlpaWn2rFmzbJ/PF+loUatOzGMBAABqh6gfYwEAAGoPigUAADCGYgEAAIyhWAAAAGMoFgAAwBiKBQAAMIZiAQAAjKFYAAAAYygWAADAGIoFAAAwhmIBAACMoVgAAABj/j/iDvbnhCX5TwAAAABJRU5ErkJggg=="
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"###############\n",
|
|
"# For testing #\n",
|
|
"###############\n",
|
|
"final_labels, final_centroids = k_means_once(X_sample, centroids, .1)\n",
|
|
"plt.scatter(X_sample[:,0], X_sample[:,1], c=final_labels)\n",
|
|
"plt.scatter(final_centroids[:,0], final_centroids[:,1], marker='x', color='g', label='final centroids')\n",
|
|
"plt.legend(loc=\"lower right\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 1.1.5: Computing Loss\n",
|
|
"Generally, the K-Means algorithm is non-deterministic, as its solution depends on our choice of initial centroids, which is usually randomly initialised. Therefore, it is possible for solutions from certain runs of the algorithm to outperform the others. One way to determine how 'good' a solution is involves evaluating the *loss function*\n",
|
|
"\n",
|
|
"\\begin{equation}\n",
|
|
" J(c^{(0)},..., c^{(m - 1)}, \\mu_1, ..., \\mu_K) = \\frac{1}{m} \\sum_{i=0}^{m-1} \\lVert x^{(i)} - \\mu_{c^{(i)}} \\rVert ^2\n",
|
|
"\\end{equation}\n",
|
|
"\n",
|
|
"where $K$ is the number of clusters, $x^{(i)}$ is the $i$-th sample's value, $c^{(i)}$ is the cluster which the $i$th sample is assigned to, and $\\mu_j$ is the $j$-th cluster's centroid. For example, if there are only two clusters, where the zeroth cluster's centroid is $\\mu_0$ and the first cluster's centroid is $\\mu_1$, and there are three sample points $x^{(0)}$, $x^{(1)}$ and $x^{(2)}$ such that the first two points are assigned to the zeroth cluster while the last point is assigned to the first cluster, then we have\n",
|
|
"\\begin{equation}\n",
|
|
"\\begin{split}\n",
|
|
" & J(c^{(0)}, c^{(1)}, c^{(2)}, \\mu_0, \\mu_1)\\\\\n",
|
|
" &= \\frac{1}{3} \\Big[\\lVert x^{(0)} - \\mu_{c^{(0)}} \\rVert ^2 + \\lVert x^{(1)} - \\mu_{c^{(1)}} \\rVert ^2\n",
|
|
" + \\lVert x^{(2)} - \\mu_{c^{(2)}} \\rVert ^2\\Big]\\\\\n",
|
|
" &= \\frac{1}{3} \\Big[\\lVert x^{(0)} - \\mu_0 \\rVert ^2 + \\lVert x^{(1)} - \\mu_0 \\rVert ^2\n",
|
|
" + \\lVert x^{(2)} - \\mu_1 \\rVert ^2\\Big]\n",
|
|
"\\end{split}\n",
|
|
"\\end{equation}\n",
|
|
"since $c^{(0)}$ = 0, $c^{(1)}$ = 0, and $c^{(2)}$ = 1.\n",
|
|
"\n",
|
|
"In particular, we can deem a solution of the K-Means algorithm to be 'better' when it gives a lower loss value. Intuitively, we can see that the loss value is lower when the data points are closer to the centroids which they have been assigned to.\n",
|
|
"\n",
|
|
"Therefore, it is meaningful for us to implement `compute_loss`, which returns the loss of the solution given by `centroids` and `labels` for `X`.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 77,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.903227Z",
|
|
"start_time": "2024-04-13T11:07:38.900641Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def compute_loss(X, centroids, labels):\n",
|
|
" '''\n",
|
|
" Computes the loss based on the current assignment of clusters.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" X: np.darray\n",
|
|
" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
|
|
" number of features which each sample has. In other words, the `i`th sample\n",
|
|
" is given by `X[i]`.\n",
|
|
" centroids: np.darray\n",
|
|
" An `n_clusters * n` matrix where `n_clusters` is the number of clusters\n",
|
|
" and `n` is the number of features which each sample has. In particular, \n",
|
|
" `centroids[j]` represents the `j`th cluster's centroid.\n",
|
|
" labels: np.darray\n",
|
|
" An array of `m` values, where `m` is the number of samples, that indicates\n",
|
|
" which cluster the samples have been assigned to, i.e. `labels[i]` indicates\n",
|
|
" that the `i`th sample is assigned to the `labels[i]`th cluster.\n",
|
|
" \n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" The loss based on the current assignment of clusters.\n",
|
|
" '''\n",
|
|
" # TODO: add your solution here and remove `raise NotImplementedError`\n",
|
|
" # print(X)\n",
|
|
" # mapping of labels to centroids\n",
|
|
" mapping = centroids[labels]\n",
|
|
" distances = np.linalg.norm(X - mapping, axis=1)\n",
|
|
" return np.mean(distances ** 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 78,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.941113Z",
|
|
"start_time": "2024-04-13T11:07:38.937280Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "0.7142857142857144"
|
|
},
|
|
"execution_count": 78,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"assert compute_loss(np.array([ [3, 4, 0], [33, 6.5, 50], [0, 0.3, 0.4], [30, 2.8, 50.4], [30.3, 2.9, 50] ]), \n",
|
|
" np.array([[0, 0, 0], [30, 2.5, 50]]), \n",
|
|
" np.array([0, 1, 0, 1, 1])) == 10.15\n",
|
|
"\n",
|
|
"###############\n",
|
|
"# For testing #\n",
|
|
"###############\n",
|
|
"compute_loss(X_sample, final_centroids, final_labels) # 0.7142857142857144"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 1.1.6: Finding Better Clusters\n",
|
|
"The following code snippet `init_centroids` is used to select the initial centroids. You\n",
|
|
"**MUST** use it in your implementation for this task, as mentioned in the problem statement. **DO NOT** modify `init_centroids` in any way as it might result in inconsistencies in the testing of your code."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 79,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:38.990600Z",
|
|
"start_time": "2024-04-13T11:07:38.987832Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def init_centroids(X, n_clusters, random_state):\n",
|
|
" '''\n",
|
|
" Initialises the centroids that will be used for K-Means, by randomly\n",
|
|
" picking `n_clusters` points from `X` and using these points as the \n",
|
|
" initial centroids.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" X: np.darray\n",
|
|
" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
|
|
" number of features which each sample has. In other words, the `i`th sample\n",
|
|
" is given by `X[i]`.\n",
|
|
" n_clusters: int\n",
|
|
" No. of clusters.\n",
|
|
" random_state: int or `None`\n",
|
|
" Used to make the algorithm deterministic, if specified.\n",
|
|
"\n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" An `ndarray` with the shape `(n_clusters, n)` such that the `i`th row\n",
|
|
" represents the `i`th randomly chosen centroid.\n",
|
|
" '''\n",
|
|
" # no loop allowed\n",
|
|
" rng = np.random.default_rng(random_state)\n",
|
|
" n_samples = X.shape[0]\n",
|
|
" sample_indices = rng.permutation(n_samples)[:n_clusters]\n",
|
|
" return X[sample_indices]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"To complete our implementation of the K-Means algorithm, let us implement `k_means` using the various functions that we have previously implemented.\n",
|
|
"\n",
|
|
"This function is very similar to that in task 1.1.4. However, instead of returning the cluster assignments and centroids after performing K-Means clustering once, we will perform clustering for `n_init` times, where `n_init` $\\geq 1$, giving us `n_init` solutions. We will then choose the best solution (i.e. the one with the lowest loss), and return its cluster assignment and centroids.\n",
|
|
"\n",
|
|
"To choose the initial centroids, you **MUST** use `init_centroids` provided above. This function takes `X`, `n_clusters` and `random_state` as inputs, where `random_state` is used to make the initialisation process deterministic, if its value is specified. The output `centroids` of this function is such that `centroids[j]` gives the initial centroid of the $j$-th cluster.\n",
|
|
"\n",
|
|
"In addition, you are to implement your solution between the `\"\"\" YOUR CODE HERE \"\"\"` and `\"\"\" END YOUR CODE HERE \"\"\"` comments, and **NOT** to modify the rest of the given code."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 80,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:39.023625Z",
|
|
"start_time": "2024-04-13T11:07:39.020186Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def k_means(X, n_clusters, threshold, n_init=1, random_state=None):\n",
|
|
" '''\n",
|
|
" Clusters samples in X using the K-Means algorithm.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" X: np.darray\n",
|
|
" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
|
|
" number of features which each sample has. In other words, the `i`th sample\n",
|
|
" is given by `X[i]`.\n",
|
|
" n_clusters: int\n",
|
|
" No. of clusters.\n",
|
|
" threshold: float\n",
|
|
" Threshold that determines when the algorithm should terminate. If between\n",
|
|
" two consecutive iterations the cluster centroids' difference is less than\n",
|
|
" `threshold`, terminate the algorithm, i.e. suppose `c_i` is the ith\n",
|
|
" centroid in the kth iteration, and `c'_i` is the ith centroid in the\n",
|
|
" (k + 1)th iteration, we terminate the algorithm if and only if for all \n",
|
|
" i, d(`c_i`, `c'_i`) < `threshold`, where d is the distance function.\n",
|
|
" n_init: int\n",
|
|
" No. of times to run K-means.\n",
|
|
" random_state: int or `None`\n",
|
|
" Used to make the algorithm deterministic, if specified.\n",
|
|
" \n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" The cluster assignment for each sample, and the `n_clusters` centroids found. \n",
|
|
" In particular, the cluster assignment for the ith sample in `X` is given by `labels[i]`,\n",
|
|
" where 0 <= `labels[i]` < `n_clusters`. Moreover, suppose c = `labels[i]`. Then,\n",
|
|
" the `i`th sample belongs to the cluster c with the centroid given by `centroids[c]`.\n",
|
|
"\n",
|
|
" If `n_init` > 1, then the labels and corresponding centroids that result in\n",
|
|
" the lowest distortion will be returned.\n",
|
|
"\n",
|
|
" Note\n",
|
|
" ----\n",
|
|
" If `n_init` is greater than 1, the labels and centroids found from the run\n",
|
|
" (out of `n_init` runs) which gives the lowest distortion will be used.\n",
|
|
" '''\n",
|
|
" best_centroids, best_labels = None, None\n",
|
|
" lowest_loss = np.inf\n",
|
|
"\n",
|
|
" for i in range(n_init):\n",
|
|
" curr_random_state = None if random_state is None else random_state + i\n",
|
|
" initial_centroids = init_centroids(X, n_clusters, curr_random_state)\n",
|
|
" # TODO: add your solution between the next two lines of comment and remove `raise NotImplementedError`\n",
|
|
" # no loop allowed\n",
|
|
" \"\"\" YOUR CODE HERE \"\"\"\n",
|
|
" labels, centroids = k_means_once(X, initial_centroids, threshold)\n",
|
|
" loss = compute_loss(X, centroids, labels)\n",
|
|
" if loss < lowest_loss:\n",
|
|
" lowest_loss = loss\n",
|
|
" best_labels = labels\n",
|
|
" best_centroids = centroids\n",
|
|
" \"\"\" END YOUR CODE HERE \"\"\"\n",
|
|
" \n",
|
|
" return best_labels, best_centroids"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 81,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:39.246418Z",
|
|
"start_time": "2024-04-13T11:07:39.121536Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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"
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
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}
|
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],
|
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"source": [
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"# Public test case 1\n",
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"X_116 = np.array([[0.63696169,0.26978671,0.04097352], [0.01652764,0.81327024,0.91275558], [0.60663578,0.72949656,0.54362499], [2.93507242,2.81585355,2.0027385 ], [2.85740428,2.03358558,2.72965545], [2.17565562,2.86317892,2.54146122], [4.29971189,4.42268722,4.02831967], [4.12428328,4.67062441,4.64718951], [4.61538511,4.38367755,4.99720994], [6.98083534,6.68554198,6.65045928], [6.68844673,6.38892142,6.13509651], [6.72148834,6.52535432,6.31024188], [8.48583536,8.88948783,8.93404352], [8.3577952 ,8.57152983,8.32186939], [8.59430003,8.33791123,8.391619 ], [8.89027435,8.22715759,8.62318714], [8.08401534,8.83264415,8.78709831]])\n",
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"output_labels_116, output_centroids_116 = k_means(X_116, 5, 0.001, n_init=5, random_state=2)\n",
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"expected_labels_116 = np.array([4, 4, 4, 0, 0, 0, 2, 2, 2, 3, 3, 3, 1, 1, 1, 1, 1])\n",
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"assert np.all(output_labels_116 == expected_labels_116)\n",
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"\n",
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"# Public test case 2\n",
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"expected_centroids_116 = np.array([[2.65604411, 2.57087268, 2.42461839], [8.48244406, 8.57174613, 8.61156347], [4.34646009, 4.49232973, 4.55757304], [6.79692347, 6.53327258, 6.36526589], [0.4200417, 0.6041845, 0.49911803]])\n",
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"diff = np.abs(output_centroids_116 - expected_centroids_116)\n",
|
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"assert np.all(diff < 0.00001)\n",
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"\n",
|
|
"###############\n",
|
|
"# For testing #\n",
|
|
"###############\n",
|
|
"n_clusters = 3 # feel free to try other values\n",
|
|
"final_labels, final_centroids = k_means(X_sample, n_clusters, .1, n_init=5)\n",
|
|
"plt.scatter(X_sample[:,0], X_sample[:,1], c=final_labels)\n",
|
|
"plt.scatter(final_centroids[:,0], final_centroids[:,1], marker='x', color='g', label='final centroids')\n",
|
|
"plt.legend(loc=\"lower right\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Testing the Implementation in 3D\n",
|
|
"\n",
|
|
"The following code has been added purely for your convenience. You **DO NOT** \n",
|
|
"have to use it, if you choose not to."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 82,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:39.253460Z",
|
|
"start_time": "2024-04-13T11:07:39.247574Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def visualise_clusters(X, labels):\n",
|
|
" '''\n",
|
|
" Visualises the clusters of `X`, with `labels` indicating the cluster which\n",
|
|
" each sample point in `X` belongs to. \n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" X: np.darray\n",
|
|
" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
|
|
" number of features which each sample has. In other words, the `i`th sample\n",
|
|
" is given by `X[i]`.\n",
|
|
" labels: np.darray\n",
|
|
" An array of `m` values, where `m` is the number of samples, that indicates\n",
|
|
" which cluster the samples have been assigned to, i.e. `labels[i]` indicates\n",
|
|
" that the `i`th sample is assigned to the `labels[i]`th cluster.\n",
|
|
"\n",
|
|
" Note\n",
|
|
" ----\n",
|
|
" This function only works for `n` = 2 or 3. In addition, to ensure that the\n",
|
|
" clusters are easily visually discernible, the visualisation only works\n",
|
|
" with 5 or fewer clusters.\n",
|
|
" '''\n",
|
|
" n_axes = X.shape[1]\n",
|
|
" if n_axes > 3:\n",
|
|
" raise Exception('Unable to visualise clusters with more than 3 dimensions')\n",
|
|
" \n",
|
|
" COLOURS = np.array([[220,20,60], [255,140,0], [153,102,255],\\\n",
|
|
" [51,204,51], [30,144,255]]) / 255\n",
|
|
"\n",
|
|
" if np.any(labels >= COLOURS.shape[0]):\n",
|
|
" raise Exception('Unable to display more than 5 clusters')\n",
|
|
"\n",
|
|
" c = COLOURS[labels] \n",
|
|
"\n",
|
|
" fig = plt.figure()\n",
|
|
" ax = None\n",
|
|
"\n",
|
|
" if n_axes < 3:\n",
|
|
" ax = fig.add_subplot()\n",
|
|
" ax.scatter(X[:, 0], X[:, 1], c=c)\n",
|
|
" else:\n",
|
|
" ax = fig.add_subplot(projection='3d')\n",
|
|
" ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=c)\n",
|
|
"\n",
|
|
" ax.set_xlabel('Axis 0')\n",
|
|
" ax.set_ylabel('Axis 1')\n",
|
|
"\n",
|
|
" if n_axes == 3:\n",
|
|
" ax.set_zlabel('Axis 2')\n",
|
|
"\n",
|
|
" plt.show()\n",
|
|
" return\n",
|
|
"\n",
|
|
"def generate_synthetic_data(n_samples, n_features, n_clusters, random_state=None):\n",
|
|
" '''\n",
|
|
" Generates synthetic data that contain `n_samples`, where each sample has\n",
|
|
" `n_features` and belongs to one of the `n_clusters` clusters. If `random_state`\n",
|
|
" is not `None`, the data generated will be deterministic.\n",
|
|
" '''\n",
|
|
" if n_features < 1:\n",
|
|
" raise Exception('At least one feature is needed to create the synthetic dataset.')\n",
|
|
" elif n_clusters < 2:\n",
|
|
" raise Exception('There should be at least 2 clusters.')\n",
|
|
" elif n_samples < n_clusters:\n",
|
|
" raise Exception('No. of samples should not be less than the no. of clusters.')\n",
|
|
"\n",
|
|
" samples = np.zeros((n_samples, n_features))\n",
|
|
" cluster_means = np.tile(np.arange(n_clusters) * 2, n_features)\n",
|
|
" n_samples_in_cluster = n_samples // n_clusters\n",
|
|
"\n",
|
|
" rng = np.random.default_rng(random_state)\n",
|
|
"\n",
|
|
" for i in range(n_clusters):\n",
|
|
" is_last_cluster = i == (n_clusters - 1)\n",
|
|
" start_index = i * n_samples_in_cluster\n",
|
|
" end_index = n_samples if is_last_cluster else (i + 1) * n_samples_in_cluster\n",
|
|
" samples[start_index:end_index] = rng.uniform(cluster_means[i],\\\n",
|
|
" cluster_means[i] + 1, (end_index-start_index, n_features))\n",
|
|
"\n",
|
|
" return samples"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"A simple test case for your K-Means algorithm."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 83,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:39.343663Z",
|
|
"start_time": "2024-04-13T11:07:39.285528Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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wySEZKyEhVvLLy8swGAxITU3FRRddJGiHjhBCYrFY0NHRAa/Xi8zMTK7ou1XqT6oRiVQJNSIJBkVRSE9PR3p6OkpLS31ajU0mE4aHh6FWq306wsJpNU6UGkm87pEIi8Ph8JkNkhqykAhAsBW4wPoBT1p+xYSiKJjNZvT09KCyshIVFRWCP+1FI4osy2JsbAwjIyOoqalBeno6lpaWYLVauf0dRFTIky5Bqk+tUhY3IYTEn0CtxmTP/czMDPr7+5GcnMz9DomtSLB7TAQhifXaBD4kPZ6enh63e9gKWUiiJJTZEKVSKXpE4vF4YLPZsLKyggMHDnDDaEITaUTi8XjQ1dWFlZUVHDx4EGlpafB4PEhLS0NxcTH3pGu1WmE0GjE4OMjZrGdnZ3NFSZnQiYU9ilKp5H5HgK+r8fj4OGw2G9LS0nxcjfmtxomS2oq32NntdqSlpcX1HoIhC0kUhLICFxA/tbW0tISOjg4AQHl5uWgiAkQmJCsrK2hvb0daWhqOHDkCjUazIULjP+lWVFT4HEhjY2Ow2+0YGRmBzWaLeYtqohIPny1/V2O3283VV4aGhuB0On1ajeXUVmjIQrINCdfmRCwhYVkWExMTGBwcRHV1NZaWlkQ/XMMVErJh0b9rbKsIw/9AOn/+PNLT0+FyubgWVVJfyc7OFrSTKFyk+kQtBcNGjUaD/Px85OfnA/BtNe7t7YXb7cbIyAhWVla4Olm8D21/Ylls3+z6TqcTqampcbuHrZCFJEwisTkRQ0jcbje6u7uxsrKClpYW6HQ6dHZ2ip5C43eGBYOmaW7DYjQDkASycZC0qDocDs60cHx8HAqFgnvK9a+vvF+RgpD4499q/Oabb0Kn02F1dRWTk5NgWdanIyw1NTXuPwM/IqFsM6CcZrDJuWDTimJyfZvNBgByjWS7EMkKXGA9jyxksX1paQkGgwHp6elcqgiITXdYKBGJ3W6HwWCAUqkMaksf7nX5/zs1NRWpqakb6ivz8/MYHBz0KfjqdDpBtg0mIvE+hINB7i0/Px/p6embthr777kX42einBYoFjpAuVfBpuSB1u8F1OupJIZhkKzwQN3+71DNvQV4HIA6Bd7Ci+Cp/xKgEfeAt9vtACCnthId/9mQSLYXCnHAsyyL8fFxDA8Po7q6GmVlZRsOWLEL0qTFeDOMRiO6urpQXFyMmpoaQdMUm113q/pKd3c30tPTOVERsr4i5QYAKUYk/vg87W/SasxvwNBoND6RZzQuDQSFtQ+q/l9DYZ/D324EyqwaeHb/A9iUfNA0jZy5P0K1/CZYbR5YbS4o9wpUE88BlBKehq9HfQ/BcDgcSE5OlrRLsnTvTCL4z4ZEYgcthJC43W50dXVhdXUVLS0tyMrKEuU6W7GZWDEMg8HBQUxPT2PPnj0oKCgQ/Lqh4l9fcblcXBqst7fXZ34l3vUVMUkUIQnWpMJ/QPBvNe7r60NKSopPxBJ25Em7oBr+AxRrZjDZuwBKAdAeKBb7oRp7Bp76L0DpNCNt8TzYlFywyespWjZZD7AsVHNvwlvzKbApwn7e+dhsNqSkpEj6dykLySZsNRsSDtEe8IuLi+jo6EBGRgYuuuiiTb8ssZhXCSQkTqcTHR0d8Hg8aG1tFa0oGOnTf1JSEgoKClBQUMDVV8gaW359hUQs26W+IuVoCXiv4SLUqDVQqzE/Ddbd3R201TgQiuVRULYpMBnl6yICAEo12JQCKKw9gGsRSrcVCsYJVrPD9/416es1k7UF0YVEyoV2QBaSgAi9N0SpVMLlckV0H2SAL1Aqy59YpLb8i+1kSj0nJwcHDhwQrbtFyOVfpL7CXwpltVoxNzeHgYEBJCcnc6ISylOuVJ8UpR6RkM9qpOlPlUqF3Nxc5ObmAgjcakzMJ8me+w2fT9YLivGCpXyPQlahAuV1gmK8cCqywKpSQbmXwWrzuL9DuZcBdSrYlPyI7j9USOuvlH+XspD4EepsSDhEEpG43W50dnbCbrfj4MGDyMzMFOU64UJqJHyRq6urQ3FxcVjvlVRMGzerr5AVtvz6SnZ2NjIzMyXXnroZUhcSfrpYCPxbjfnmk7Ozs1xKkwhLeno6kF66XvOwz4JNL+FeS2GfB62vB5ush0uZgbX8i6E1vgiGZcFqMkC5V6BwWuGp+hhYba4g978ZUrdHAWQh4RBzBW64B7zVakVHRweysrJw5MiRkPO+saqReL1etLW1wWazhSxyQlw3FgSqr5A0WE9PD7xeL7KysriIJV67WUJB6kISbUSyFVqtFlqtFjt27PBpGV9cXORajXU6HYpTDiB34SWorH2AOnW9cytZD7rswwC1ni627/w00tOzoJx+GQqHEaw6FZ7qT8BTd7Mo986HuANIGVlIIJ0VuCzLYnR0FKOjo6ipqUFpaWnYT/lip7Y8Hg8mJiag0+nQ2toas5Wt8dqDkpSU5DP3wK+vjI2NgWVZqNVqzM7OSq6+InUh8Tc3FRP/lnF+q/GktRYTjB3ZzgFkql1QZh+EpuKD0Oh2gfrbfSo0qfDs+SI8VR8D5bSsF9uThXHU3gq73S7XSKROpLMh4RBKEdzlcqGzsxNra2sRP+WLGZGwLIvp6WlYLBbodDo0NTVF7SobyT3Ek0D1lcHBQSwvL0dcXxGTRBGSeNzjxlbjfVhZWeEiluVeIzTDi9DpdHC73VzrP5KzwcZIQAgOh0OOSKQKfzZE7BW4W5k2klSWTqdDQ0NDxIePWEJC0zR6enqwsLAAvV6PzMzMmH/5pXggKhQKJCcng6Zp1NfXB6yv8DcNxrq+InUhkZLzL9mxkpWV5dNqbLVaQdM0urq6om81jhC5a0uiMAwDi8UCj8cDnU4nqogAmx/wLMtiZGQEY2NjqK2tRUlJSdRP+UI/tftPqY+OjgomVuH+rPGOSDaD/Bzh1ldi0YkjZSGRghniZvBbjaenp9HU1MTNI/FbjflDrmINDMqpLYnBnw0xGo1wOp1Re0CFQiAh4aeyDh06hIyMDFGuEw3z8/Po7u72mVKXd7b7Euy98K+v2O12bjCS2H/w968IYSXDR+oW7ZK9P9cSlIt9AMvCq6sDwzDQaDTIyMjY0GpstVoxMDAAl8vl02osZPRpt9tRVBQbX69Ied8Iib/NidD+V8HwP+AtFgs6Ozuh0+nQ2Ngo2JOMUEISbEo9XkICSDciCQWKopCWloa0tDSuvrKysuIzv6LVan0WQgmROpHkQf03pJTaIiinX4G6/zeg1kwAWKiScpHn2gOl8mKfvxdOq3F2dvaW2z+D4XA45IhECvBnQ8gOdZVKFTMhIaLFT2VFMnuxFVv5YIXCVlPqobr/Co2UD8RI4OfkAfjUV0ZGRrC2tsbt7Yi0viJ14ZVaRKJYHIC6+2GAcYPJrARAAatTqFj+M1RLVwO5+6Awt0PT+T9QLLSDTSuBZ/cX4K24fstWYwCcq7FOpwvL1Vjqu0iAbS4kwWZDYrVHnVyLpmm8++67cDqdgqWy/In2kA9lSl2OSMRhs/qK1WrdUF8J1V5d6sV2qdVIlPNvg3IvgdHtAgBQtilQy8PQrxlBvfFVeCtuQNL5HwKgQLFesCvjUM2cgav523A3f5t7nUCtxmTPvcViwcjICJRKZci2PPIcSRzZajYklqmtlZUVeDweJCUloampSbSiXKTiyJ9f2SpSkiOS2CBEfUXqQiK51NaaBVCoAYqCYrEfSrMBLMuAYWkoFwegtNwLsAworD/QUOz6+aFpux+eus+CTd0R8GUpikJGRgYyMjJQVlbGpTUXFxcxPz+PgYEBJCUl+XSE8V2NHQ6HpHeRANtUSBiGgdvtDjobEgshYVkWw8PDGBsbA0VR2LdvX1y6w4Lh8XjQ2dkZ8pS6EOmzSJFqRBKLzqtI6itSFxKppbbYjHJg2gN4HVBYe8ECYFVagHGATc6Cwj4b8N+jWAaqqZdDnnIP1GpM1h5MTU2ht7cXKSkpGB4eBgAsLy+LapHi9Xrx/e9/H7/97W8xPz+PwsJC3HLLLfjud78bstBvKyEhqSyPx7PlbIjYQkJqDW63G42Njbhw4YJo1yKEm3ZaXl6GwWDgdqmHUtyVu7Z8icd7Eai+QvLx/PoKqQNKLYVEkNp90UUfgHLqL1CaO0B518AqNaBoF2iFGpQqeGqJpSI3K1UqldDr9VwHqcfjwdLSEs6cOYPf/va3mJiYwDe+8Q288cYbuPzyy3H06FFBi+/3338/fvazn+H//u//UF9fj/Pnz+Nzn/scMjMzcfvtt4f0GttGSMK1ORHTct1sNqOrqws5OTlobm7mogSGYUTd/RyOFcv09DT6+/tRWVmJioqKkA/qeAqJVCOSeOPvgkv2ok9NTcFut+P111/3WQYlhfW1gPRSW6w2F+7mb0Fj+A8oVkYBlgGjSoWLTUGKOgWsQgMwbvi/cyylAl16lWD3oVarkZubi29961u46667UFpair/7u7/DxMQEvvzlL+Pw4cP4/e9/L9j1zp49i2PHjuHaa68FAJSXl+P3v/89zp8/H/JrbAsh4UchoS6e2mraPBIYhsHw8DAmJiawa9cuFBcXAwDXciwFIeFPqUeyS10+0KUP2YvudruxsrKCiooKH38w/0Kv0PMroSK11BYAsBkVcF30Eyis/VCsjIFOygXr9gAss24l7/GCBQWKpcFSKlCsF67W+8Bqc0S7J6fTiRMnTqCurg7AequxkBw9ehQ/+9nPMDg4iJqaGnR0dODNN9/EAw88EPJrJLSQ+M+GhLO9UOjUFr9t9vDhwz7FMfLUFQtn3mDXsNvtaG9vh1qtjniXejyL7bKAhQd54if1FbK+lr9lsL+/H1qt1scfLFYrXaWW2uJQauBq/RGS3/g6lPZ5JNE0KIcabEYFnAe+DdX0aSgWOsCmFcOz+/OgC1pFuxWPxwOXy+WTyhLaGPSuu+7C8vIy6urquHPx3nvvxac+9amQXyNhhcR/BW64H0gSkQhRkDSbzejs7EReXh527dq14YtIXj+Wu0L8f6ZAU+rRXCMeyEISPv6fA7INUqfTYefOnZvWV/jWH2Id9lJLbfFh8g9i7cOnYO/4HZwLw8ivPgRv+bXr9vICprG2wmazAYCoXVuPP/44Hn30Ufzud79DfX09DAYD7rjjDuzYsQOf/exnQ3qNhBMSoVbgkhQTTdMRP4ExDIOhoSFMTk5i9+7dm9oYUBQVky4x8qXkCwnDMBgYGMDMzAz27t3LTeJGipCRQThPpFJLgfCR6r2F8pC0WX3FarViZmYGDMNwg3RC11ekmNriw6bkw1pyApYUC7Lr9sflHux2OwCIOtn+zW9+E9/61rfwyU9+EgCwd+9eTExM4L777tueQiLk3pBo001ra2vo6OiA1+tFa2vrlgNDsdpeCLx3QDudThgMBtA0LdgudSGExOv1oqurCyaTCZmZmcjOzoZer9/SxFCKEYkU74kQyb2R+gp/foXUV0ZHR6FSqQSrr0g2tcUj3vfocDig1WpFra06HI4NP2O4NeSEERKhV+CSNy6SKMFkMqGrqwv5+fnYtWtXSL/kWG0vBNYPEDKlnpubi927dwv2QYxWSEidRqPRoKmpCTabDRaLBRMTE9yQHfkPfyhLrpGET7SpI/78ihj1FSmntghiN8hsBbGQFzNyu/7663HvvfeitLQU9fX1aG9vx09+8hN8/vOfD/k1JC8kYq3AjSTdRMwMp6amUF9fjx07Ak+yBiKWEcno6CgmJyd9OseEvEakP4fJZEJnZydKSkpQWVnJGdsVFRX5DNmRQyolJYUTFVLPkgkdod8v//oKmXfg+4Px969kZGQEFQqpp7YAcA+u8SIWFvL/8z//g+9973u49dZbYTKZsGPHDnzpS1/CP//zP4f8GpIWErFX4IYjJGtrazAYDGAYJqRUVjTXihSPxwMAmJubE22XeiSRAZnwHx8fx969e1FQULBBjPhDduSQIrn6/v5+uFwuJCUlcU/AUpmFABK7RhINZN6BX18habCuri6uvkIiFv/fmRyRbA0REjF/j+np6XjggQfCavf1R7JCIpUVuMB7qayCggKuRS6Sa4kZkZApdQBobGwURUSA8IXE4/Ggo6MDDodjQ1t0MNRqNfLy8pCXlweWZTEwMMDt2CazEPw0WKx2xycSsbZISU5Oxo4dOzgHXFJfIUaF/PoKiTJj1WocKTRNx3VdciIstQIkKCT+syHxXIFLOp7IXo7CwsKIryWWkLAsi6mpKQwMDKCyshLDw8OiPkGFIySrq6toa2tDWloaWltbI/5CUhQFjUaDtLQ01NXVcbl6q9XKeRORbXXEa0rqT7qxIJ5P/JvVV0jqsq+vD0qlEqmpqcjIyEBWVpYkRSXexfZEcP4FJCYk/rMh4QwYRkKwdJPD4UBHRwcYhsGRI0eifioQQ0gCTamPjY2JGvmEKiSzs7Po6elBRUUFKisrBfk9kuvyc/WVlZU+2+p6e3vh9Xp9nnxTUlJE+xxJvW4jlbQb/3cGvGcWyrIshoaG4HQ6w6qvxAqppLakjiSEhNRCLBYLNwAViy/AZkJiNBrR1dWFHTt2oLa2VpAPktDeXptNqYs9MLjVZDt/bqWhoYHLnwtx3c3gb6sLlFJRq9U+abB4pipiiZSL2Wq1GhqNBllZWSgpKeE2DPrPr2xWX4kV8S62J8J2REACQkJExOFw4Ny5c7jiiiti9oHxFxL+IVhfXx9VKivQtYSKFMiUeklJCaqrq30+6GJbmAQTKpfLBYPBsOl2RX9IdBPq7zsUgfRPqdA0zaVUJiYm0NPTw01uR7p5MJGQqpAAvkLnv2GQ1MP49RX+/hV+a7jY9xjviERObW0BfzaE5Edj+YvjRwkOh4MrVh85ckRw/38hUluhTKmLXdTfLLW1tLSE9vZ26HQ6NDc3h5TvDkdEIp0j4RflgXWxIwdUd3c3GIbxSYNptdqwD1+pHtZS30eyWQ2Hoiikp6cjPT190/pKamqqz/4Vseor8Y5I5BpJEALNhpBfltfrjVnqgUQJ5Al/x44dqKurE+WDE+0BH+qUejyEZGpqCv39/aiurkZZWZmkD6+kpCQUFBSgoKCAe/K1Wq0wm80YHh6GRqPhREWn0yV0Gkzq9ZtQU2+B6ivEH4xfXyG/MyHrK/GOSBwOB7d+WcrERUgoigJN0xuWT8Vy/S2w/gGdn5+Hw+HAnj17UFBQIOq1Ij3gw5lSF3sCnP/6NE2jr68PJpMpIkv6SK8r5GuSJ9+ysjJuUx1ZZ8tPg+n1eqSnpydUGkzqcxqRdkTxW8MB+NRXpqenuSiTRCzRNFvEu2tLLrZvQaBfTiyFxG63w2w2AxAnleVPJMV2/i71UKfUYxGRMAzDDWgCiNiSPtzrio3/pjqXywWLxcKlVFiW3ZAGk/JTv5TvDRBO6ALVV/znVyKtr9A0HfcaidT3tQNxFJJAB4NKpeLmR8Rkbm4OPT09SElJQWZmpugiAqwfUmTyPBTcbje6urpgs9lw6NAhZGRkhPTvxUpIzp49i7y8POzevTtmT2yxPhiTkpJ8BuxWV1dhtVphNBoxODiI5ORkKJVKJCUlwev1Sm4OQuo1EjG6ygJFmcQfbHp6Ouz6ihyRhIakPvliRyQ0TaO/vx9zc3PYu3cvVldXBd82thnhHPDLy8tob29HRkZGyLvUCWKmtliWxezsLFiWRXV1NUpKSiJ+rUQraFMUhYyMDGRkZKC8vBxerxdLS0sYHR3F8vIy3njjDS5PT9Jg8b7nRBASsQ9pfrNFZWVl0PpKdnb2hvRlvIvtckSyBYE+4GIKid1uh8FggEKh4FJZDocjZqm0UITEf0o9nF3q4VwnErxeL7q7u2G1WgFAEDPIcH82KaVqVCoVcnJysLi4CGD9/bBardy0PQCf2ZV4rLOVupDEo4YTTn1Fp9PFvdhut9tjkjGJFklFJGKltsiUdUlJic92wFjWZLY64L1eL3p7e2GxWNDc3My1qwp9nUjgDz8eOHAAb7/9tqCvHwpSPhCB9Tx9UVERioqKwLIs52Q8NzeHgYEBzmyS5OpjcThJXUjinTYCNq+vLCwsYGRkBAAwPDyMnJwc6HS6mM2vAOCGa+WIJEyEPthJV5HRaMT+/fu5pxCxrheMYNey2WwwGAxQq9VobW2N6ulV6IFEYv1eVFSE2tpars4Tj0NKShFJMCiKQmZmJjIzM1FRUcGts7VarRgcHITL5QproVekJIKQSOn+/OsrLpcLb731FpKSkjhPt9TUVO5hIBb+YHKNZAvETm3ZbDZ0dHRwqSytVrvh7whtWxKMzSKF+fl5dHV1obS0dMOUeqTXEeLAZVkWIyMjGBsb89m94r+FMVZI6cDhE8k6W4fDwaXBtlroFS1Sfd8A6bcnE6qqqkBRlM9qg1DqK0LgcDjkgcRwESq1RVJZWx3OQtqWbIW/kPCn1Pft2xf1LvXNrhMJxFDPZrNtsH7nb2GMNYkSkWxFSkoKUlJSUFxcvGGhF+kq4jsZR5oGkyOS6KBp2sc4NlB9hexf4ddXSMQSrVmo2+2Gx+ORU1vhEm1EslUqS+jrhQP/gOdPqQs9wxJtamt1dRXt7e1ISUkJ2DEWLyGR8oETDZst9LJYLOjv74fH40FmZib0en3YC72kLCQsy0o+Itmq0O5fF/N3SVCr1T7CEm6kabPZAECOSIKxWWrL5XJF9HqkzqBSqTZNZQW6XiyFhKZpLCwsoKOjI6x97+FeJ9JDfm5uDt3d3SgvL+fCeX/kiGQjQh7W/gu9+Gmw0dFRbrgulIVeUhcSIPBgslQIp/V3s/kV/s6ccOsrdrsdFEXJXVvholKpIjrYZ2Zm0NvbG3adIdapLZfLhfb2dlF2qfOvE+7PRHbRT09PbxnJxTMikaqQiAVFUUhNTUVqaipKSkrCXugl5feLv3NIqkRTB/Q3C+XXV/gNFyRiCVRfIa2/UhZbgqSEJNwIwev1cl5Pkey+iFWx3e12Y2hoCF6vF0eOHAl5Sj0SiI9ZOPdmMBjgdrtDtn4X26peJjCBFnqRaCXQQi8p1yASJSIRKmMQrL4yNTXlY79D6is2m030PSwzMzO466678MILL2BtbQ01NTX45S9/iebm5rBeR3JCEmqxfXV1FR0dHVCr1bjooosiapklEYmYKQAypZ6SkgKVSiWqiADhpbbIvWVlZaGpqSnkVsZ4RAdSjUjieU8ajcbHydh/oRfLspiengZN05Jb6EUeRKQsJGJ2JvrXV1ZXV7G4uAiz2YyBgQH8v//3/5CXlweGYWA0GkUxlF1cXMRFF12Eyy67DC+88ALy8vIwMjKCrKyssF9LUjWSUFJbLMty3S1lZWWoqqqKKvwE4LMPRSj4U+pVVVXIycnBO++8I+g1AhFqaov4DlVVVaG8vDwsIZXqof5+JtBCr7Nnz0KlUm1Y6KXX6wVfZetiXGhztGHeM49MVSZaUlqQrty82ygRUluxMmzk2++UlZXB7Xbjnnvuwa9+9SvYbDYUFRVhz549+NCHPoQ77rgjKmsiPvfffz9KSkrwyCOPcH9WXl4e0WvFNSLxP5C2Sm2R6e+FhQU0NjZG7dMvlpB4vV709PTAarVyU+pra2ucdb6YX56thIRhGPT19WF+fj5i63ehhCSc15HFKzyUSiUUCgWKioqg0+ngcrm4NFhXV5cgC70IZo8ZD5gewJhrDCzWf0dPqp7EV/K+gtrk2oD/jtQ7toD4Td5rNBrccMMNcDqdsNvt+NOf/oTTp0/jL3/5i6DfgWeeeQZXXXUVTpw4gddeew1FRUW49dZb8YUvfCHs15JUaivYHMnq6ioMBgOSkpIEsy0nXxwh6yT8KfUjR45wLX/kAym2kAQ7cJ1OJ9rb28GybMidbZtdI9Y1Eik/uUr13viftaSkJBQWFqKwsHBDq+rQ0BCSkpIiXuj1W+tvMeIaQa4qF2pKDZqlYfKa8AvzL/Cjoh9Bo9jYWSYFe5StiLfPFqmR5OTk4MSJEzhx4oSgrz86OoqHHnoId955J+6++26cO3cOX/va15CUlISbb745rNeSlJAEikhInre/vz9oW2okUBQlaOcWaZ8N1D0Wq4nwzSISq9UKg8EQ0nKsUK4R7ZMRwzCYnZ2FVqsNeW+6HJGEz2Yt3KEs9CKzK8Emtq1eK7rWupCuSIeaWhcfJaWEXqXHvHceA64B7NXu3fDvSbkRgBBv51+xp9oZhsGBAwfwox/9CADQ2NiInp4ePPTQQ4klJJultsiTFEkRWSwW0TbwCTFLwp9S36x9lhzcYj/J+wsJy7KYmJjA0NAQamtrUVJSEvUXONo0E2mDdrlc8Hq9XMcKObgCRZtSP3SkSKjRr/9CL6fTucHJ2D8NRlhj1kCDhobyjTpUUIFmaawxgdc0yKmtrRHbZ6uwsBC7d+/2+bNdu3bh5MmTYb+WpCISUqegaRoOhwMGgwHJycm46KKLRHPdjFZIQp1SFyONFgi+kNA0zVm/HzhwgNt5HS3RCMny8jLa2tqQnZ2N/fv3g6Io2Gw2WCwWzik3JSWFExX+bIQUIxIp3hMh0jRqcnLypgu9DMMGUMkUSrNKkZ+dD32WHjmqHMy556BVvCcwq8wqUhQpqNBUBLxGIkQk8U5t2e12USOSiy66CAMDAz5/Njg4iLKysrBfS1JCQn5pk5OTGBkZQUVFBSorK0X9wEUjJOFMqVMUJfr2QnIdMhHd3t7OTfoLbQQYyQFKBkerqqpQVlbGRSOkY6WiosLHIoQ/G5GUlCTProSJEPU40lHEprI4k3YG7Y52OL1OpHhTUD9Zj53dO7E3ey/mtHOYYWaQqkqFm3GDpVhcm3EtctWBZ7vi/bQfCvFes2uz2UT12fr617+OI0eO4Ec/+hE+/vGP49y5c3j44Yfx8MMPh/1akhISclCMj4+LlsryJ5LDne+MG86UeiyERKFQwO124+zZsygsLERdXZ3gX9hwi+1kcn5mZobrtttMiPwtQux2OywWC4xGIxwOB86ePctFK7Ha65GoCNXYQbM0Hll4BJ1rnchQZiBTk4kV5QrOJ51HVUUVPrT2IaQspuAN+g0sqBaQrcjGJSmX4NqUa4Pem9SFhGGYoBY0YuNwOESZHyG0tLTgySefxLe//W3cc889qKiowAMPPICbbrop7NeKe42EsLKyAoPBAIqisG/fvpiICBB+ROJ2u9HZ2QmHwxHWLnVAfCFhWZY7cPfu3YuioiJRrhNOROJ2u9HR0QGXy4XDhw+HlfPlz0akpaVhaGgIVVVVsFgsGBwchNvt5gwN9Xp91G6rkSLVFI1Qabch1xD6nf3IVeVy6asURQpmPbN4y/UWLtlxCU4UncBHmY9iZWVl3QpkwYpzo+e4hV56vd7HyTgRUlvxLrbHYhfJddddh+uuuy7q14l7RMIf3Nu5cycmJydj+pQZjpAsLS3BYDAgMzMTra2tYU8Ki2nJ4vF40NXVhaWlJSQnJ4smIkDoXVvESTgtLQ2HDx8WZFYnJyeHi2jW1tZgsVg4Q0O1Ws1FK9nZ2aIvHZI6QkUkM+4Z2Gk7UhWpYFgGCmr9cE1TpMHsNWONWUOqMtXHyZikKUk32MDAAFwuF7KysiRv30LY7jUSIYnrN83j8aCjowNLS0vc4N7c3Jwo63Y3IxQh8Z9SD3cSnH8tMSISm82GtrY2aLVa1NfXo7+/X/Br8AklIjEajejs7NyyZTvUw87/7xBX1JSUFJSUlPi0sI6OjqKnpwcZGRlctCLmFkKpIoSQtDva8eTSkzDRJlhpK5IVyShWF0On0sHJOJGtykayIvBMl1qt3nShl9VqBcuy6OnpEWWhlxBIISKRhSQExsbGQNM0LrroIi4XGUtrd2DrKCHQlHo01xJaSMiGxbKyMlRXV2N5eTlmBf1A8OtHe/fuFTTHG+zQ5rewVldXc6Z4/C2E/GglnrnvWEDeq2iEZNI9iUcWHoGDcSCVSsUas4Y1Zg3j7nG4WBe8rBdH045CSYX21M5f6DU9PY35+XlotVqfhV7kd5SZmRn3+le8GwIcDkdCrNkF4iwk1dXV3BYyQqyFJFiUQKbUNRqNIJ1PQgoJy7IYHBzE1NSUz4bFWFiJbHYNr9eLrq4urKysbNisKNY1N4Nvise3X5+cnERvby83cKfX60VZkRpvhBCSs7azWKaXUaIuQbYqG5PuSThoB9aYNSzTy7gh6wZ8KONDEd+fRqPBzp07Nyz06uvrg8fj4dJg4S70Eop4prZIo4kckYRAoIM1HAdgIdhMuMiUerTGkHyEEhJSwHY6nTh8+LDPhy1WnWH+13A4HGhra4NGo0Fra6uknvj97df5vlOdnZ1gWZY7sPR6veRSLNEQzeFr8pqgolSgKApaaFGTVAM7Y4fRY8R+7X58KvtTEb+2f9eWkAu9hCLeqS2x23+FRDJdW4RIl1tFilKphMfj4f4/wzDo7+/H7OxsSOt6w71WtD8bsX4nBX//gnIsZ1UIZJ5mx44dqK2tFeXLJ2Sk5e87tbq6CovFgtnZWQwMDHCb7PR6fUj2LVIsGgsRkRSqCnEe57laCwUKqVQq1Ao1dibtjOr+ghXbQ1noRZyMSRpMjM9cvIvtYlukCInk2lrikdpyOp0A1pfNGAwGztRQ6BWX0R7yZKCPpAM281GKlZDw7VfE3PpIrinW6/oPRJIn4Z6eHtA0zdmD6PX6DUaXUi22CyEkrWmteNP+Jma8M9Ar19vxLbQFOqUOrWmtUd1fOPWHYAu9enp6Niz0EqoNPJ4RCUltyTWSCIl1aosc7mLvUudfK1xIlDQ3N7elfX4sXIbJFsauri5YLBa0tLREtAwnXGJxaKvVauTn5yM/P9/HJddkMmFoaGjDXIRUEUJIijRF+Iecf8ATi09gzjMHACjTlOFjuo+hWBPdQ0M0A4lbLfRSq9U+abBIF3rFMyJxOp2gaVpObYXCZqktl8sVs3tQKBRYWVlBe3s7du/eLfr8RbhCwvfyam1t3TJK4rsMi/UlYBgGExMTSEpKQmtrqyCW/lsRr0FDvkuu1+vl9m4PDAzA7XZDpVIhLS2N268tlTSXEEICAHu0e7AreRem3dNgwaJEUxJyl1YwhJojCbTQi6TByEKvjIwMTlTCWegVz64tu90OAHJqK1JiGZG43W5MTU3B6XSitbVVdPUPdyBxcXERBoMBer0e9fX1IQmD2EJCDtK0tDQcPHgw4muQ3e/hEO80kkql4uYiSEG4t7cXa2trePfdd7mBSL1eD51OF9eBSCHfKyWlRFlS+EZ+wRDLIkWpVHKiAWDLhV6bPZiRFdzxFBKKoiLeGRRrJCcksSq2kyn1pKQkpKWlxSSEDHUgkWVZTE5OYnBwEDU1NSgtLQ350CV/T4xDl6znTUtLQ15eXkzDfqk86RP4BWGtVovS0lJuIHJkZARra2vIzMzk0mBiDURuhlARiVgwDBMToQ1loRffu43cE/mexiu1RVp/pfr780dyqS2xi+38Q7qqqgopKSkYGhoS7Xp8QolIaJpGT08PFhYWIhqA5EckQsGv0TQ1NWF2dlaw1w6HeEckm0EWpAUaiLRYLJiYmOCelMmhFWnePlQSQUhifW/BFnoR8SdpMPJgGa+IhGxHlOrvzx/JRSRiprb4U+pkP4fFYomZPblCofBpNfaH7GBRKBQRrxMmKSOhfia32w2DwQC3283VaObm5mJ+qEv1C7XZ+xBoIJKICj9vr9frkZGRIfjPJ/ZK52iJ99Q4sNENIdBCL76FSyzTTIk01Q5IQEj85wPESm3ZbDa0t7dzO9/J0Fks242DFdtJ11hBQQF27doV1ZdMKCFZXV1FW1sbMjIy0NTUxIX98djZDkg3ItkKfvsq8F7e3mKxYHp6GgB8ohUhBiKlLiRStJHnL/RaXV3FhQsXkJ6eDqPRiMHBQa5jjyxcEzM1Z7PZJNW8sRVxFxJ/xDjYg02px1JINttJPzY2hpGREcFmMYTYqU48vALNrAg1HBjOlyRRvlCh4J+3X1lZgcVi4Tyn0tLSwhqI3Awpv2dSd/9lWRZKpRLl5eUoLy+H1+vF0tISLBYLhoaG4HQ6uRoYSYUJ+fMkkj0KIEEhUalUgqW2+Ln9YLvU4xWREG+q5eVlHDx4EJmZmaJcJxxYlsXw8DAmJiY2fc+EEpJwXyNRI5JgUBSFzMxMZGZmcp5TJFrhD0SSaCXU9IrUIxIppLaC4b8dUaVScSsMAPiYgk5OToKiKJ9usGhb4kmNJFGIu5D4H0rkYI/2i8CfUg82f0Ge3mPxweYf8CTVlpycjCNHjgjqHRSpkHi9XnR0dMBut2/w8PJ//Vi6DwDSfroWkkADkWRDJEmv8PfZb9ZVJHUhkfr9bXUe+NfAyF77ubk5DAwMICUlxScNFm73l1wjiRKlUsnZb0T6QTObzejs7AxpSp2/sS1WQmI0GtHV1YWSkhJUV1eLsgo33Kd3u92OtrY2JCcnb7m0KxYOw4GQakQipoMA6TIi6RXikNvf38855BJh4efUE/2gjjfhzGEpFAouqiQ2O/zBVf5Cr+zs7JDaeuXUVpSQApbX6w37KZ2kZcbHx0OeUicfFpqmRe9rVygUsNvt6OzsFHxXh/91wolIzGYzOjo6UFJSgpqami0/5PEQEqkeirF8HwINRFosFs4aRKPRcKKiVCol+54B0iy284nGZ8vfyZifBhsfH4dSqfRJgwVqrrDZbLKQhIP/h5388sJNnRBr9bW1tbB2YZB2WbFTNW63G6Ojo/B4PDhy5IioA5ChCgnLshgfH8fw8DDq6+uxY8eOkF4/Hl1b8YqCpAp/IJJYg5BiMJmJAICJiYmQn4JjSSJEJELcH3+TZ3FxMRiGwcrKCqxWq8+AL9/JWKlUwm63c11+YnPffffh7rvvxu23344HHnggoteIu5D4Q4a7wjnY+bvUjxw5ElZkQVGU6Dl/4uWVlJSE5ORk0afoQzl0aZpGd3c3FhcXwy70C9EVFilST9nEC/5MBLDedTc4OIjl5WXuKTiWA5FbIfWuLbEshvh77fnNFVarFX19fTh//jyeeeYZOBwOHD16VPTP+7vvvouHH34Y+/bti+p1JCckQOidW/wp9erqapSVlUlqlzoAzM7OoqenBzt37oROp0NnZ6co1+GzVUSytraG9vZ2KBQKtLa2hj23IKe2fJHivWk0GqjVauzbt2/TgUj+hshY/wzbObUVDv7NFfn5+bDb7Xj44Yfxq1/9Cs888wyuvPJKXHvttThx4oSg17bZbLjpppvwi1/8Aj/84Q+jei1JCkkoEYnX6+WeqMmUupjXCxeGYTAwMICZmRk0NDQgNzcXy8vLMel2CiYki4uLaG9vR15eHnbv3h3Rl0UoIXE4HPB4PGEdZHJEEhr89ynQQKTFYuEmuCmK8rFdj8WGyERIbcXaZ4uiKFRVVeHuu+/GuXPncMMNN2Dfvn146aWX8OqrrwouJLfddhuuvfZafOhDH0p8IYnEb2uzKfVIEVpIXC4XDAYDVw8hrcdiRj58NjvoJycnMTAwgNraWpSWlgr++uEwPT2N3t5eAAjJNVcWj/AIJrhJSUncBDdpXfUfiCQpMDG3D0r5dxrvNbsOhwM6nQ6XXXYZLrvsMsFf/7HHHkNbWxveffddQV4v7kISiGCpLZIqKisrQ3V1tSAfRiGFZGlpCe3t7cjOzkZzc7PPoRiLNbiBrsMwDPr6+mA0GiMygvQnmmI7y7I+kVpaWho32U2KxKSlVa/Xb7CJkFrBXWr3wyeU7wa/dXXnzp0+2we7u7vBMIzPPnuhds9IPbUV7zW7Ym5HnJqawu23346XXnpJsN+nJIUk0MEeypR6pAhxwLMsi6mpKQwMDGxaryFFarHTM/yfh0RHXq8Xra2tghjPRVpsJwOPDocDhw8fRnJyMjweD3dQEddc0tI6OjrKtbRKeRuhFIn0M+a/fZAMRJLifagDkVuRCBFJvPbJkK2PYjXlXLhwASaTCc3Nzdyf0TSN119/HT/96U/hcrnC/r3GXUg2S23xI5JQp9QjJdqIhKZp9PX1wWQyoampieuc8ScW2wuB91JPy8vLaG9vR1ZWFg4cOCDYNSNJbTkcDrS1tSEpKQmHDx+GWq0O+J5rtVoUFxejuLh4Q0srAHR2diInJ4eLVqJl1UxhaU4JrwdIy2ah20FD9bdMqccF2BYUUGtZpGVLN/IIhBAPK4EGIkm0QgYi/ffZh3JN8jAl9Ygknvcn5kDi5Zdfjq6uLp8/+9znPoe6ujrcddddEZ0TcReSQPAdgMmUekFBAerq6kQ5gKMREtIBRVHUltbv/OFHMYVEoVBgeXkZw8PDqKysREVFhaBPf+EKidVqRXt7O3bs2IHa2tqQv6D8ltaKigq88cYb0Ol0WFhYwPDwMJKTk7l/HsnT8fyQElOdKnhcgEIBzLNAZoEClQc9mGhXY+htNdxrFCgFkFNGo/EGF1KzNv7cUnyyFiPqValUPoN2ZCCS/D7Ikijy+9jsiZ58dqQuJNs1tZWeno49e/b4/Flqair0ev2GPw8VSQoJiUiGhobCmlKP5nqRCInFYoHBYEB+fn5IHVBiLJ3yh2VZrK6uwm63o7GxEbm5uYJfI5waCRm6qqurQ0lJyYZ7DVWQyKFYVFS0qV0IMTckT8fBWFuhMNOjgkLJQle4fg+0F1iaVaL7JQoTHetzFqokFiwNGIeUOPu7ZFz2xTUoJfmt8UXs9GmggUhiC0LccYktiF6v91nSRD47UhRgQjyL7SS1JU+2h8FmH6b5+XkoFIqwptQjJdyBRP5EeKADcjOEXjrlj8fj4ab7CwsLRRERILSIhGVZ9Pf3Y3Z2Nmi6L5xr8vG3C7Hb7bBYLDCZTBgaGkJKSgonKoE6j1YXFHA5gKwCnmGoClBrGUx2qcGyQHLa3/6ZClAoWawYFTAOKbFjV2wNKyMh1k0ASqXSxx3X4XBwabCxsTGoVCru90EOSDkiCYzD4QDLsjFZ/004c+ZMVP9+3IXEn6WlJczOzkKlUoU9pR4p4bTlkvmVpaUltLS0hF0EFqtzy2azoa2tDampqSgsLBT1S7pVsZ0vaIcPHxY0RA90XYqikJaWhrS0NJSVlXG5fL4VO3ky1uv16+3iLBDoEYZlAK+Lgkbr+ztS/O1jaLMoALwnJFLt2or3vI2/LQhZaTs2Nga73Q5gvR09XgORWxHPiMThcACAHJFEAn9KnTy9xqprQqlUBl2BS7Db7Whvb4darY5oIpxcS+ihRJPJhM7OTpSWlqK6uhqDg4OiDj4Gi0gcDgcuXLgArVbLFdUDwbIsaJoGTdNcYZPY1Wx2zVDxz+WTziNi8Z2amor0pAIwilI4V9XQZqz/ewwNeJwKaFJY0B4KqqT3fkbmb2+nNlOawuFPvIWEj0Kh4DrzqqqqsLS0hLa2Nthstg0DkXq9XtCVCpESz4jEZrNBqVTGZDBUKOIuJBRFbZhSt9vtmJubi9k9KJVKOJ3OoH+HHNbFxcWoqamJ+GlFyIiEZVmMjo5idHQUe/bsQWFhIXeNUIQxUjYTklCL6kREyL3y/z/5M/Lvkv/m26OHe6/8ziP+4ii7sh8LE3nQqLVI1mqgVmmRVcAgq4DBwBtqeNbWxYRhAM8ahbRsFoW1wixdExspCYk/arUaCoUCe/fuDWhimJ6e7rPPPh6RQTwjElJol3Lqz5+4C4nH48HZs2e5BU9JSUlwOp0xXZwULErgW9PzD+tIEUpI+Cm2Q4cOISMjg/tnYrvzBhKSqakp9Pf3b1kzYhiG+49SqeSiToZhuOiEZVmu/TtYlBIJfG+jujoWsyNrmBlew+qSFU6lFSqtF7n5mSheqcBcbxpcdgoKBZCZz6Dloy6oAjwsS/HAlrKQ8Ft//U0MyUCkxWJBV1cXWJb12RAp1ADdVsSz/TfRLOQBCQiJWq1GTU0N8vLyuA++/xyJ2Gx2uHs8HnR2dnIbA4UofgkhJA6HA+3t7VwdyT8VILY7L1+oiKfY7Oxs0Kl50qHFj0T4Bx0/CuGLDV9ggPXfCXmijRaFgkJxdQqKq9fnUdzuXG4Y0pnzBjIOapHM7EB2Xioq6lOhSYqvY264SFVIgg0j+g9EbrZ5kLQYi3XYx7vYLvSsnNjEXUgAcB8aAn+OJBYEikhWV1fR3t6O1NTULTcGhkO0lvWk5biwsBB1dXUBv0hiW7GQiIQU1Z1OZ9BBUbLKmN/2GeyQ8xcVl8uFnp4epKWlQa1Wb4hWhIpaNBoNCgsLUVhYyKVc1oVlBm+9vT5prNfrkZOTI7n9Hv4kSkQSDIqikJGRgYyMDC4tSVq++/r6fFq+yYZIoZBCakuqv79ASEJI/BGjIB3O9ebm5tDd3Y3y8nJUVVUJ+guN1LiR34ywVfpI7NQWEap33nmHK6oHGz4jEUUkBz4Z+CRDVGQJGRGnQLUVYaKV91IulZWVnGOuxWLB5OQkNywZ6zRsqEhZSCK1R/HfPGi322G1WmE2mzE0NITk5GQuWtHpdBFHFCR6jmexXU5tRYB/zj3WqS0iJAzDYHBwENPT04L7eREiiRYYhkFvby9MJlNIlvlip7ZWVlbg8XhQVFSE2traTQ8Fvoj4p7JCwWq1oqOjA8XFxT6Czo9WyDXIl1+saMXfMZfs91hbW8PIyAgsFgvXXiyFp0mpC0m0vxN+y3dpaSm8Xi9npzM4OMjtSSfRSji/E376NR44HA7RptrFQhJC4o9KpeIOiVh8GRQKBbxeL86fPw+3243W1lbRfpHhConT6YTBYADDMFtasER6jXCYnJxEf38/FAoF6urqNv170YrI9PQ0BgYGUFdXt6mrAfmikydHEqHEIloh+z3sdjsyMjKg0WhgsVgwNjYWki2+2EhZSMTw2VKpVAEHIon5p1qt9olWgqWqyXcnnsV2WUgEgO9JFYsv4draGmfp0NTUJOo1wznkl5eX0dbWBr1ej/r6+pBDbTFSW3z35fr6em6XiD8kMiA1kXBFhGVZDA0NYXZ2Fo2NjWFZ3vvXVvhixq/RkHuiKAq0RwHrlBKrCxRUaiBrB4PMAgbhnMEajQZFRUUoKirihu9CtcUXC6kOSgKx6YjiD0TSNM1FkGNjYz4bIrOzszcMRMY7Ikk0exRAIkLi/8UiB6bX6xVdSMiCJYVCgf3794v+JQ+1/jMzM4Pe3l5UVVWhvLw8rPsSOrXl8XhgMBjgcrnQ2toKILBfmH9RPVwRoWkaXV1dsNvtaGlpieqpbLNohQgMwzBwr1EY/asay/MqKBQAWArzQyyK93hRvCeyugd/+K66unrDkzGxxY82j78VUnbXjbWFPNlXTx5KnE4n9zuZmJjgfmdEWCKNooVCFhKBIE+WYhYxSd3BaDSivr4e3d3dMUujBYsW+O20jY2NXKgu5DXCwW63o62tDSkpKVxRfW1tbYNQ8WsV5B7CgaTwVCoVDh48KFiXHCFQtDLXq8DSrBIZeV4o/naeu2wKzPQqoStikKqLXoz9n4xJ19Hg4CDcbveGaEUopByRxFvkkpOTfepdpDtvamoKvb293O9haWkpLgORdrs9am+6WCNJIQHE7dxyOp1ob28Hy7I4cuQI9wQfi5A72CHvdrvR0dEBl8sVlUeVUEJCWo39i+r8KXPSKBFNPWRlZQUGgwF6vR67du2Kye8AAJbn1NCmAeokgGXWU3JJ6QyW55SwzjHQpHm3LNiHc2DzjQ0D2bCTpVFCzEhIuUYS710ffPy789xuN6ampjA1NRW3gUi73Y6ysjLRryMkkhCSUJZbCYXVaoXBYEBubi52797t47MVTyFZXV1FW1sb0tPTg7bThoIQO9XJfvddu3ahuLh4w+sD7x2iJBKJRERMJhO6u7tRUVERdgpPKCiKAqXkiaOCgoJa/xyIVbD3t2Hn2+KTGQl+uiXczZZSFxKp3ptGo4FOp4PZbMahQ4e4ffb8gUj+hkgxzgu5a0tAhB5KZFkWExMTGBoaQm1tLUpKSnwm6YHYFPcVCgXcbrfPnxmNRnR2dgo2txJNRMIvqm/Wakzuz+v1cteKpKg+MTGB0dFR1NfXIz8/P6L7jQZ9CYPxdiW0NMultpw2BZK0gL5ICY1G41P3CdReLFQKaTNbfKPRiMHBwS1t8QMh1cM63qmtrSDDiPyByIqKCp+ByN7eXni93rB24ISKPEciIEKmtmiaRnd3N6xWa8DDkXxoYjFYxh9IZFkWIyMjGBsbw969e1FQUCDINSIVEv+i+mY5e3JAeTweaDSasEWEiJXZbEZzczMyMzPDvlchyK+msWyksDSnAKVkwdIUlGoWxXtIfSR4e7HX64XH4wFN05yoChWt8G3x+QfYprb4fsgRSeRslpkINBBpsVh8BiJJtBJNI4UckURIoA+VSqUSJLXl70u1mTVzrKbpySHv9XrR1dWFlZUVwZd3RfKkbLfbceHCBaSmpm45qc6yLFJSUvDWW28hOzsbubm5yMnJCSl/TPzLPB4PDh06FDMTvkAkpQB1l3ixMKnAqpmCSgPoihhkFQZ+7/hC4fF40N/fD5qmodfrN20vFkJY/A8wm82GhYUFH1t8IiqkOCx1IZFyRBKKz1agHTj8gUi3243MzMyI2r7lri0BEeJgJ/veQ9kVHql1SbiQ1NY777yDpKQktLa2Cr5/Idw5koWFBRgMBpSUlKCmpmbLSXUAaG1thcPh4A60/v5+pKamcqKSmZm54XWIqKekpODAgQNxGdTzR50MFNYwKKwJ/d9xu90wGAygKAoHDx6ERqMJ2F4MCD9hz7fFJ+kWYt1CisPZ2dlwuVySeH8DkSiprXDgD0SyLIu1tTVYLBZYrVZuIJJEK9nZ2UEf1Ox2e0y3IwqBND9piK7Yzt/TUV9fjx07dmz574jdbkxwOBxYXFxEaWnpluIWKeGktiYmJjA4OIjdu3dvOkEOBJ5UJ09kxFBvYWEBCwsLaG9vB0VR3BdLr9fDZrOho6MDhYWFQcVK6hAxTEtLw549e7gn12DDkFvtWokGtVrt45ZLWlkXFxexvLyMxcVFzmhSKpsIEyG1Fc18D0VRXNt3SUkJaJrmNkSOjo76DESS1cP898PhcMgRiVBEWmz3eDzo6urC6urqhj0dwRA7tUWKy5OTk9Bqtdi1a5do1yKpjWDpDYZh0NfXB6PRuKV/F38yfLN6iFqt9nHOXV5exsLCAsbGxtDV1QUAyMvL29ABlkgsLy+jvb19SzHcahhSLD8wiqKQmZmJzMxMrK2tISkpCampqVwbN0VR3FOxXq8XfFYnVOJp0R4KQjv/EoNPvV6P6upqrK2tcfvsyUCkXq+H1WrFzp07OfdfMbjvvvtw6tQp9Pf3Q6vV4siRI7j//vtRW1sb1etKQkg2a/8N92Ane8tTUlLCThmJKSQ0TaOnpwcWiwVVVVUwmUyiXIfgP+fhD5lXIb5im3WbRDqpTryosrKyQFEU1tbWsGPHDjgcDi6lR1JgOp1O0mkOgtlsRldXF6qqqlBaWhrWv+sfrfjvWvH/e0K8HyzLbmqLPzk5ib6+vqBPxWIi9dSW2EKn1Wp9LHWWl5dhtVrx//1//x9eeOEFMAyDX/ziF/jkJz+JgwcPCpqifO2113DbbbehpaUFXq8X3/nOd3DllVeit7c3KvGShJAEIpT1t3zm5+fR1dWFsrIyVFdXh/2lEEtIyPAjsF5XWFlZwfz8vODX4cM/tPy/sERs09LScOjQoS3t3yOdVCedcqurqzh48CAXqtM0zQ3h9fT0wOv1cqmXnJwcSe6pnpqawtDQkCBtyv6iAkCUaMW/2SKYLf7ExITPU3OwHL4QJEJqK1ZCxzcA/f3vf4++vj4cOnQIMzMzuOGGG0DTNB588EF86lOfEuR6f/7zn33+/yOPPIK8vDxcuHABl1xyScSvK1khCTW1xTAMhoaGMDU1hX379kX8RRejRrK4uAiDwYCcnBxu+NFms4le1Pc/qAjhFNUjNV0EAJfLBYPBAIVCwRWjCUql0qcDaXV1FQsLC5iZmeH2dZNoJd45fbJmeWZmBk1NTcjKyhL09f1rJUJGK1t1bW1mi09MDfkdR0Lb4ku9a4um6bil/UiK+dFHH4VGo8GFCxeQm5sr2vWWl5cBICxz1EBIQkgiTW2RFI3T6cThw4ejKlAJ3bU1PT2Nvr4+1NTUoLS01Gf4UeyiPrkWf16FLMWKpKgeDqurqzAYDNDpdNi9e3fQA4M/8LVz507uKdlsNmN8fNynE0av18c0r84wDHp6erC8vBy1gWSobFawj2TXSjjtv/yn4qqqKi6HL5Yt/vs9tRUMu90OtVqNpKQk7kFMLFiWxZ133omjR49iz549Ub2WJIQkEFvNkZDCZ2ZmJlpbW6P+cAt1wPMnw5uamjaYrwlpqLgZ/BqJ0EX1YJA6Qnl5OSoqKsL+9/2fkhcXF7GwsMAtKtLpdMjJyUFubq5gU8SBICuEaZpGS0tLXNJtmxXsw9m1EmkUwc/hk44jIW3xpZ7aiueaXbKLJBbvz1e+8hV0dnbizTffjPq1JCMkgbYkbnawE4v1ysrKiA6sQAghJGS+gBSxA02Gx0pIFAoFXC4Xurq64PF4tiyq82dEIrE7mZqawvDwMHbv3i3IhD7pZNHr9aitrYXdbsfCwgLMZjNnGcKfWRHqi09W+2q1WjQ2NkqmuyhYe3GghgihDmt+7QQAZzTJt8XPyckJa5pb6qmteEcksYh+v/rVr+KZZ57B66+/LkgnpWSExJ9AcyT8p/1ILdY3I9oaycrKCtrb25GRkRF0OVYshIRgMBi2vB//ojqZyA4VYntvMplEtTshBofEMoTs6u7o6ADLslwKLCcnJ+L89urqKtrb25GTk4O6ujrJHnZbtRfTNA2Px8P9uVDtxQA2zEf42+KH4j0l5al7IL4RCRESsd4flmXx1a9+FU8++STOnDmDiooKQV5XskLiX2wn+ypomg7qAxUpfBfgcCEdYzt37sTOnTuDfgiIkIj5ZTKbzWAYBnq9Hnv27BGtqE5mdlwuFw4ePChquomPWq1Gfn4+8vPzwbIsN7MyMTHBFYpJCizUL6XFYuGMM+PlQhwp/GjF6/Wir68PXq8XWVlZog5DbmaLT7ynNrPFT4SIJN5CIha33XYbfve73+Hpp59Geno610GamZkZ1fdXMkISLLVFup/CXTkbDuG2GwPvrYWdnJzE/v37kZeXF9J1AHHCZ77DsUql8nE4DvR3oymqkxRQcnIyWlpa4mbHQVEU19ZaVVUFp9PJpcBI6oU/sxLoPZ+dnUVfXx92796NwsLCOPwUwkA2TDqdThw8eBBJSUkxHYYMZovPd8qN5xN/KMQztSW28+9DDz0EALj00kt9/vyRRx7BLbfcEvHrSkZI/CGprfHxcQwODqK2ttan+0mM64WT2iLmg3a7PayOMf5TmZAfVrLx0WQyoaWlBQaDYdMUWrRF9aWlJRgMBhQUFKCmpkZSh0JycjKKi4u5jYRWqxULCwvo6+uD2+3eMLMyOjqKycnJsPfDSw2Px8NZ0xw4cIBL78VjGBIIbotvt9sxNDSE5eXlsGzxY0U8hc7hcAiebeEj1uZMyQoJOdxGR0fR0tIStNtICMKpkZD1s1qtFq2trWHl5Deb8YgGt9uN9vZ2eL1erqgeyAE42qI6AMzNzaG3txfV1dVhT3jHGqVS6XOY8V1z+/v7oVKpwDAMdu3aJfrnS0zI0KtWq8XevXs3fUAJVLAPtmtFyGiF75T79ttvo6CgAE6nM2Rb/FiynSMSsZCMkPiblpFp8JaWlpg4YYYakZAC71ZDfZtB/r5QsyQ2mw0XLlxARkYGmpubuRSTf1E/2qI6McIkaTwhGx1iAd81t6SkBAaDAQ6HA1lZWejv78fg4KDPzIpUnXP9IQ812dnZYa0pDlSwj1W0QhyKs7KyuKFUi8WC2dlZH1v8nJwcZGRkxLxeFe9iuywkArCwsICOjg4UFBRgdXU1Zk8GWw0ksiyLsbExjIyMhOwoHAiKogQbfiSiVlZWtmGzIl9I+PWQSJ4yaZpGb28vlpaW0NLSkpAfdILL5UJ7ezvUajUXTZLJbrPZjJGREXR1dfnMrIiZaogGMktVVFQU9WZNIYcht4I/kOi/hdDtdnPDkJ2dnZzokGhF6JULgdjOxXaxkIyQkCfekZERbk/43NxcTKzdgeARCfGNWlxcxMGDB6NucY22BZhfVN9M1EjzQrRFdTIbA4Ar4CYqNpsN7e3tG6bu+ZPdNTU13J6VhYUFrvuIiIpYe7rDxWKxoKOjA5WVlSgrKxP0tYUYhgxGsBkXjUYT0BafOEXwjSbFsNAhP2O8UlsOh0NUSxSxkIyQEL8s/kEdq62Fwa5FupMUCgVaW1sFOUijERJSVDebzWhpadnU/4nUfKIREXLwZmZmitYtFytI519JSQkqKyuDvhcpKSkoLS3luo/IzEpXVxfXVk3SYLF4QvbHaDSiu7sbu3btijgyDodwhyG3ilZCtUjh2+Lv3LkTbrebG4acmpribPGJ0aQQ/lj8nyUe2Gw27Ny5My7XjgbJCElZWRlKSko2GPwJsW43FAKlm6xWKwwGA/Ly8rb0jQqHSIWEFNXJLM1ma2rJjIrNZkNOTg6USmVIIsI63aCNC2BdHiwzHnTNTKKkrHTLg1fqzM/Po7e3FzU1NWFP8apUqg0mk2azGVNTU+jt7UVGRgYXrcTCip04Ee/bty8uT65bDUOSz3WwFFikU/eb2eJPTExwv4tobfHJ/cczIpFTW1GQnJy8QTQiXW4VCf5dW5OTkxgYGODajoUkkkhrdXUVbW1tyMjIwN69e4NOqjMMg5ycHIyNjWF6epo76IIVkWmjBa6zHWDMi1haXIRp0Yq6w00oKC1LWBEhKcDR0VHs3bs36oOXn88nVuwkBcY3mczNzUV2draghxG/2UEMJ+JICRatBEqBkZRrtA9l/rb4TqeTq61EY4vP72iMB3KxPUqEWm4VKeRafJPD5uZmUWYLwo1ITCYTOjs7AxbV+fC/xCUlJSgtLQ1YRCYtsWSSlXW54X6nE7RlCSYFDauaRfne3UieW4RnYByavdWC/NyxhGVZDAwMcGaVoW7KDIekpCSfBUWLi4swm80YGBiAy+VCdnY2lwKLZmqY/CxkRkiqB81W0YrX6/VpABGyqJ2cnOxj+EmMJsO1xY80DSwUspCIwFYOwEKiVCrBsizOnTsHhmGCmhxGS6hCwrIsxsfHMTw8jD179gSdut6sqE6e2qqrq7kiMjE+TE1NXX96djJQzpsxQ7vgor2oqalBUlISaNYC78gU1PWVoCRQYA4V0hxhs9liZt3CN5kkdiFmsxlGo5FraSXRSmZmZsgHFcMw3IKwlpaWmNnQCIF/tOL1ernmBYqiuO+20O3FCoUC2dnZ3EPg2toaV1vZyhY/3lP3cteWCMQyIrHb7QDWnzL37dsnao40FCEh+zAWFha27BQLtTOLX0T2eDycL1Jv9yBSh0ZAF+eiaMcOrmhJadSA2wPQDJAgQkK6zCiKQktLS1yK4Xy7kPLycu69JovFAPjMrGxWJPZ6vejo6IDX643bzyIkfX19sNvtaG5uhkajickwJLBui893OyDRyvDwMJxOJ7KysrjfRTyFhDgAxGJuTmgkLySxiEhmZ2fR09MDAKirqxO90LbVFH04RfVIjRfVajUKCgqg1WrROT4FrT4LalUS5ubnMTk1hfS0NGSsOJHRsAuUWtIfEw4yyJqWloY9e/ZIpsuMvNekpZWkG8fGxtDd3c0dZDk5OVzahXwGVCqVz6BpIkLTNDo6OuB2u3HgwAFOEMVqLw5GMFv8kZERri5rsViQlZUV88+QXGyPkkAHoNjFdpZlMTg4iKmpKTQ0NKCtrU00Lxo+wQYSV1dXceHCBWRlZQW1u+ALCBCZ3YnRaERPTw8q99UjP68E3o4B5Cdlwk2xsM+ZsMx6MbxkQvI773B1lXivv90MMpxXWFgYkeNArOCbTFZXV2NtbY0r2I+MjCApKQlZWVmwWq3IzMzE3r17JTG3EilerxcGgwEsy6K5uTlg9BVsGDKS9uJw8LfFn5iYwPT0NAYGBkK2xRcSuUYiAtFYu28F2YK3trbGmS7GKpW2WWrLZDKho6MDFRUVQVtu/e1Owv1SkdrL2NgY183EFtFQZqXDOzSBZKcLqUeasaO2HNVZaVxdZWJiwseML9RFRmJjMpnQ3d2Nqqoqyft/+aPValFSUsIdZLOzsxgcHARFUbBYLOjq6vIxmUwkiJGkUqkMeUnYVsOQ/PZiIihCRitpaWlISUlBc3NzyLb4QkHTNNbW1mQhERqlUom1tTXBX9dms6GtrQ2pqak4fPgw95QULyHhF9X37t0bdMNgtJPqZKDRarX6+JhRKiXUteVQ1ZQBLMsV15XAhvW3ZrMZfX198Hg80Ov1nE17PHL4ZK6ivr4e+fn5Mb++kKyurmJkZIRbVWy322E2mzEzM4O+vj6kp6dzohIPD6pwcLvduHDhwpZGklsh9DDkVpAayWa2+MRJmm+Lr9frN00/hwOp08o1kiiIVWqLtNKWlpaiurra57rxEBLSlWOxWEIqqvM7XSKxOyG7yA8dOhTwCZeiKGCT1+V3JtXW1sJms20YziPRith7p1mWxfDwMGZmZiQ1VxEpCwsL6Ozs9ImqiMkkmeomKbDJyUkoFAqfmRUp1VCcTidnJFpfXy9oNxYQ3TDkVmxmjxLIFn9hYQHz8/Pc6mfy3YjUFp8IiRyRREk4e9vDhQx0jY6ObtpKG60HVqiQYjsxEGRZNuSiOhnmCveQttvtaG9vR3p6ekiFaJZl4e0fA2OyQlVXAWW+3uef8910d+7cCZfLBbPZjIWFBYyOjiIpKYn74gmdBiAdbcvLy2hpaUnI4iQfYs1fX1+/aTSq0Wg2zEkQL7C1tTWfmZV4mkw6HA5cuHAB2dnZ2L17t6gPE+EOQ4byGQxltoVvi0+68sgwZHd3N1iW9YlWQk1J2u12JCUlSeqhIFQkfcdCzZF4vV50d3djaWkJhw4d2nQ4LVYRiVKphN1ux9mzZ2NSVLdarejo6EBxcXFILrHeqXksfeVH8HQNASwApQLaj12BzHtuW28JDkBSUtKGhVL+/lQkBRaNJxKpbdE0jZaWloSrG/gzOTmJ4eFhNDQ0cJ1EW8Gfk+CbTJL5oJSUFE5UYmkySVLGeXl5qK2tjWnqjR+tkO9KJJshI2n/9V/97G+Ln5aWxolKsJSkzWYTPZIXC0kLiRAHO2kJValUOHLkSNA8fqyExOFwwGQyobKyUtSiOgDMzMygv78fdXV1KCoq2vLvszQN62e/C3pidj3FpaAAhsHaH16CIj0VGd/5wpav4b9QamVlhSvW9/T0ICsri/vn4Tw9EwNNrVYbcvFWqrAsi5GREUxPT6O5uTkqR2l/k0kys+Iv4mLasJNuw+Li4rh7s/FdnYHwdq1E6/wbzBa/o6MDAHysW/i/D7vdLtmVBVshKSEJlNqKJiKxWCwwGAwoLCxEXV3dlgex2EJCdpoYjUZkZmaiqqoq6N+NZh0u2Sc/MzMT1hpZ1+ttoMdm1gcQFX+7plIJ0DQcv30OaV//DBQpG1NwjH0N9KwZlIKCsigPVPJ6pMB3cK2qquLaXUknTEpKChepZGVlbfpzrq6uor29Hbm5uaitrU3olliWZdHX1weLxSJ4ak6lUvk8Ha+srGBhYYET8czMTC5aEcpkcmlpCe3t7VyTgNQIlALbbBjS6/UKKoKb2eLz64p6vR6Li4tYWVkR3fjzwQcfxI9//GPMzc2hvr4eDzzwAC6++OKoX1dSQuJPpAc7y7KYnJzE4OAg6urqUFJSEtK/t9WgYDTQNI2enh5YLBaUlZUF7UaLtjOLpml0dXXBbrfj4MGDYR1U9DgvEuFDUWCdLjALi1CU+taX3IZ+uF6/AMayBFAUlHnZSPrQYahryje8Pr/dlTw9kwVdAAIaTJIlR+Xl5SgvL0/I0J9A7FvsdjtaWloE6fbZDL6IE2NDUrAfHR2FRqPhRCVSk0nikJ0ordfB2ou9Xi9WV1eRkZEBt9st+DCkvy2+y+WC1WrF7Owsjh07Bq/Xi6SkJDz++OO48sorBV///Pjjj+OOO+7Agw8+iIsuugg///nPcc0116C3tzfq3x3FxmICL0Q8Ho9Psdtut+Ott97ClVdeGfJrkEKs2WxGY2NjWL+Mzs5OpKamorKyMqz73gp+Ub2xsREmkwkmkwkHDhzY8Hf5IXgkIuJ0OmEwGKBSqbB///6w6xHO0+9i8fP/7BuRAABNg0pOQt75x3wiEu/ELBy/fwGgKCgK9ADDgp42QpGmRcrfXQ9lbmjvP3/i22w2w+FwcDsmjEYj6uvrg3qNJQJkOI9hGDQ0NMTV8oSmaa6d1Ww2w+12Izs7m4sOQxE4UgOrra0NKW0qZRiGQWdnJ9bW1jiLJP7RKMYwJB+Xy4V/+Zd/wbPPPgudToeenh60trbiqaeeCrl2thWHDh1CU1MTHnroIe7Pdu3ahePHj+O+++6L6rUlH5HwD9WtIIcowzA4cuRI2E97YqS2VlZW0NbWBp1Ox3VLBZps528zBCIrqq+srMBgMECv14e1v5tP0sVNUFYUrddIGHY9OvnbvaZ8+sMb0lqevjGwdidUtX/b0qcElOU74O0fh3d4MmQh8Z/4ttvt6O/vh9Fo5OZs7HY7cnNzJT9DEQjyMKHRaCRR31EqlVw0Ultby7Wzzs3Nob+/nzP0zMnJCWgySZZrBes0SxSIiDidTs4HjPy50O3Fm5GUlITy8nLs2bMHzz//PKanp/Hyyy8L5j5O5nq+9a1v+fz5lVdeibfffjvq15eUkPh/WElqI5ROCpKn1ev1EW/zE1pIjEYjOjs7sXPnTuzcuZP7+QINJEZbVCfT3RUVFVGlfyiVEtm//iGWbrsXnp7hdTH5W9dW+jdv2fD3maVVINn3yZqiKFAqBRh7ZMOkDMNwwnHo0CEkJydzT85tbW1QKBRcsV7ovR9i4HA40NbWxm2alFp9J1A7K0mBtbe3g6Ion5kVs9mM/v7+uC3XEhJ/EeFH8OG2F5P/HSl8e5Ti4mLccsstEb+WPwsLC6BpesPQbn5+Pubn56N+fUkJiT/kgKBpOmiKZmZmBr29vaiqqorqEFUqlXC5XBH9u3z4MyuBJtX5QiJEUZ0sbxJqultVWgD9M/8Nb98oGPMiVLXlUBbkBPy7ysIceLqHwDIsqL+lwlgvDZZhocwOvxPJ6/Wis7MTLpcLBw8e5KJK/ma8paWlDXs/iLBIrR2YLCQrKCiQtAcYH7Va7fN+Ly8vc15gnZ2dAICioqKE7TAiMAyDjo4OuFyuTX3ACKHsWgGii1ZsNpvow4j+nz+yTTVaJC0k5JexWecWwzAYGBjA7OwsGhsbkZMT+LALFSEGEkkxdXFxcdOZFVLUF8LupL+/H2azOeoWUn8oioJ699a1InV9FTxdQ6CHJqDI1wMMsz7EWLZj3W4lDEj6R61W48CBA5sa/PFnKIiNyOzsLPr7+5Gens6JSixW3waD7IlP5CYBhUIBnU4HnU4HtVqN0dFRFBUVwW6345133uEGT3NycqDT6SQXbW0GERG3272liATCP1oJtb04GGI6/5KV2/7Rh8lkEubhM+pXEJBwbFKI3YfL5cLhw4cF+QVEm9pyuVxoa2sDALS2tm76dExqJNGIiMfjQWdnJzweD5f+iQfKnCyk3Hg5nG8bQE/MARQFzYE90BxthCIt9CdWm82G9vZ26HQ67N69O6QvHj8lQ3r2SQpsfHwcarWaO+Sys7NjesiRVGMke+KlBomwp6amfLZNErv1hYUF9PT0wOv1+sysSC06JNA0jc7OTrjdbjQ1NUU1IAuE114cLFqx2+2iNZRoNBo0NzfjL3/5C2688Ubuz//yl7/g2LFjUb++pIQkEIEOd5IuSE9Px+HDhwWzFIhGSAIV1QNBLE5cLhcGBgaQl5cXdH4iEGTIMiUlBQcOHIi7pYKyKA+pJ64Es2IHFFRYAgK89+ReUlIS1TAb30aEdCWZzWb09vb6HHJiG0zOzMxgYGAAe/bsQV5enmjXiQVk1cL8/DwOHDjgk3pRKpXIy8tDXl4eN9G9sLDAzUiQ6DAnJ0cy6wfIbhSv1yuIiPgTKAUWarQitoX8nXfeic985jM4cOAAWltb8fDDD2NychJf/vKXo37thBASfmqLFLDLy8tDsvsI91qRCMn8/Dy6urpQWVmJioqKLSfVU1JSsHfvXpjNZi7nnJOTg7y8POj1+qDF48XFRXR0dEhy74YiI/yocH5+Hj09PaitrRX0yZ3flVRXVwebzQaTycQdcpmZmT7T9UK9j8Sev6GhQbCOm3jhPzgZrCbCn+gmMxJkRmh8fBwqlcpnK2Q8GiT8RSQWD2DBdq34RyvLy8ui+sZ94hOfgMViwT333IO5uTmuQ6ysLLwUdCAkNUfCMMyG/SPvvPMOysrKUFBQgOHhYYyPj29ptR4p8/PzGB0dxZEjR0L6+/yi+r59+4LmGjerh5D5CZPJBLPZDKfTiezsbOTl5SE3N9fnyXl2dhZ9fX2CH7rxgN8kQHaixAoymGc2m2G1WgUxmCROAnNzc2hsbNzUzy1RIPNYKysraG5ujip1StYPkPfc5XJBp9Nx0UosFkbFQ0S2gj8MabVasW/fPnzqU5/CL37xi3jfWthIXkjeffdd5OXlwWKxYHV1FU1NTaL59ZNOoKNHj275d/lF9aampqAHR6hFdWJPTYbyVlZWOGt2p9OJ+fl57Nu3T7ABpXjBsiwGBgZgNBrjfuiSPD9xLmYYxme6PpTUB9nxsrS0hKampm3RzUSG85qamgSvdZCZFbPZjKWlJR+bnEgt2INB0zQ3X9bY2CgJEeGzvLyM66+/HhkZGfj973+fkHt1EkJIVldXkZ6ejv3794ua27Zarejq6sIHPvCBoH/P6XRy/fWNjY1Bv2jRTKq7XC4YjUaMjY3B7XZDq9UiPz8fubm5AYfEEgG+fUtTU1NMnkZDhW8waTabYbfbodPpOGEJJBCkcOt0OkU5dGMNeXL3eDxobGwUffqeWLATIWdZlkuBResUDUhfRFZXV3Hs2DFkZGTgmWeeiVvTTLRISkhYloXb7eb+/8LCAi5cuIDMzEwcPHhQ9K6b5eVlXLhwAR/84AeD/p22trYtBx/9J9WJvUI4uFwuGAwGKBQK1NfXY3V1lTvkEm0oD1jvtDMYDKAoCg0NDYIXOoVmbW2Ne78XFxe5J2ci5MTyBEBC/Dxb4fV60d7eDgBxOXRJmpcMQ9psNq6WlZOTE7bFOk3TPtZEUhMRu92Oj3zkI1Cr1Xj22WcTeq+OJIWE5M+HhoY4k7Pa2lrRr2+z2XD27FlcccUVAf95OEX1QPulw2F1dRUGgyFgOywZyiN1Ff7K29zcXEkeaKTTLD09PWLngXjCN5hcWFgAsP57Jpb2iR6JeDwetLW1Qa1WY//+/ZL4/fjXsjQajc/MSrB7JCICrIu81ETE4XDgxIkToGkazz//fEJuReQjOSFZW1vjXHIbGxthNBpB0zR2794t+vUdDgfeeOMNXHnllT4HP9kdMTY2hv379wdt6eTXQyL14iFmeMSWO5gIsSzLdSSZzWbYbDZkZWVxxXoppI6Wl5fR3t4uyU6zSLDZbLhw4QLUajVYloXT6eSKx7m5uQmXniDzT1qtFvv27ZPkUCFZlkaiFbfbDb1ez6XA+O85X0Sk4Gvmj9PpxCc+8QnYbDa8+OKLCd+YAUis/dflcuHcuXMAwK2eJR+aWEAcP/m2ASSnv7S0hMOHDwct9Ec7qc6yLKampjA8PIzdu3eH1JnGX3lbWVnpk44ZHBxEamoqJyrx6OUng3mJYjO+FWReqKioiGs/Jw0SRqOR24hHREUq8xObIdZ+daHxX5Zms9l8TCbT0tK4wdPh4WEolUo0NDRITkRcLhf+7u/+DktLS3jppZe2hYgAEhMSjUaD/Px8lJeX+wz2CLFuNxT43l4KhQJOp5MzCQw2qQ5ELyLE7sVkMqGpqQlZWVkR/QxarZbblkfM98h2QjLpnZubGxM7i6mpKQwNDQnmARZvyMpiYoxJSE1NRWpqqo/hodlsxuTkJDfPIsVaFtmvTtyipSx4fPgPT8TRwGKxwGQyYWxsDBRFIT8/HwsLCz57beKNx+PBLbfcgtnZWbzyyiuC7xuJJ5JKbQHgaiSEqakpGI3GgLs7hIZhGLz00ku49NJLuXBfr9djz549mx66JIKJxnjR4/Ggq6sLLpcLDQ0NoqSjGIaB1WrlUmD8NtecnBxBv2wsy2J4eBgzMzNoaGiIWBSlBLFN37VrF3bs2BHSv0PmJ0iESNIx5D2PZ12FpOcSyUwyGKRRgKIolJeXc2kwh8OxZeddrO7v85//PPr7+3H69OmEd032R/JCMjs7i6mpKRw6dCgm13/xxRdRW1uLoaGhLd2EhSiqkz3kycnJ2LdvX0yenvhtriaTiVsiJUSOnwyyLS8vo7GxMaE7UQjT09MYHByManAy2IyQkGtvQ4Gk50pKSnzWGyQqXq8XbW1t3DI3ftTncDi4uorVaoVWq+VEJdLh03ChaRpf+tKX0N7ejtOnTyf8/pZASF5IjEYjhoeHcdFFF4lyvVUXQLNARhJAgcVLL70EhUIRclE9mh0iS0tLMBgM3FNhvPLTDoeDi1SWl5eRnp7O1VXCabn0eDzo6OgATdNoaGhI+E4mlmUxNjaGiYkJNDQ0CJqKcLlcXArMYrFwHUlipx3J3h7/9Fyi4vF40N7eHlBE/PF6vT4zKwzD+BTsxZiZoWkaX/3qV/HWW2/hzJkzCb9JcjMkJyT+63YtFgt6enpwySWXCHqdJSfQPk9hapUCwwL6ZBpJ1j4wS5PYt29f0PQFX0QiiUIAYG5uDr29vaiurpZUEdrtdnNPzRaLBUlJSZyoBDOXJJEV6fyRUi0gEvjT92K6KQC+HUlmsxk0Tfu46Ap1wJH96tXV1SgpKRHkNeNJOCLiD4nKSbRCdrWTaEWICJFhGHz961/HK6+8gtOnTwviaSVVJC8k5AnqsssuE+waTi/w4qgC0ysU8lJZMF43OkbmkapisFc7g4saaja1IRGiM2t0dBSTk5PYu3dv1DtUxIRvH2I2mwGAe2rmG+8RN+a8vDzU1tZKtvMnVPg+U7GevicuuuQ95w/lkQgxEkhLeV1dXcg1HilD5l40Gg32798f9WeORIgLCwuwWCycyWSkTRIMw+Cuu+7Cs88+izNnzmDnzp1R3Z/UkUY7QxDE6NqaXqEwuwpUZLFwOR0YHRtFeWY6POllMFlWAzoAC1FUp2ma82RqaWmR/BCSv0042Uw4ODgIl8sFvV6PlJQUTE9PR73iVyqQDY1utxstLS2iW4T4w3fRrayshNPp5FIxIyMjSE5O9pmuD+UAJY0Ce/bs2Rbdc0KLCLC+M72oqAhFRUU+TRL8LZwkBbbVgwXDMPjud7+Lp5566n0hIoAEhSTQ3nZ+GkkIbB4AFIWVZSsmJydRUFCIvLxcGO0UrHTShi2J/kX1SESE2IMAwMGDBxOufkBRFLcpr7q6Gna7HSMjI5iYmACwbmdDURTy8vIS1rTQ7XZzqRIp7HkBgOTkZJSUlKCkpMQnx9/R0QEAPtbsgRwNyObI7bBfHVgXkQsXLiApKUkwEfFHoVBAr9dDr9eDZVk4HA6fOaHU1FQuWvH3vGNZFv/6r/+Kxx57DGfOnEF1dbXg9ydF4v9N2QL+ghih8u7JShYLCwtYsc+goqICmX8bCnJ4KWSofZfPCFFUJ9v/MjMzE9IeJBBGoxFWqxXNzc1ISUnhUjHDw8OcJ1VeXh4yMjISIkohNZ7U1NSgi8niiUql8okQl5eXYTabMTo6iu7ubp/peq1Wy83xbIfdKMB7IkI6HGORQqUoasOcENkKSR4Ms7Ozcf78eVx//fX43//9X/zqV7/C6dOnUVdXJ/r9SQXJCwl5KvR6vYJ8ub1eLxbHe6BYS0FyQR2SUjTwMMCCA9CqWBSmukDT6+2v0dZDgPVmgc7Ozqi3/0kFhmF8lh2R9Bz/qZkUjckwJxGVWK+7DRWbzYa2tjbk5OQkzGAeRVHIyspCVlYWqquruTZXknpUq9XweDyoq6vbFoNvbrcbbW1tMRWRQKjVahQUFKCgoIAT856eHvzkJz/B1772NSiVSnz1q1+FSqUSNIsidSRXbPd6vRsigpdeegkXX3xx1CkTMqmuVCpRVNsEgzkJ83aAYQFdMtCYz8I2uR45lJeXRy0iU1NTGBwcxO7du0XbxRxLSP3A5XKhsbFxy3kT/4E8j8fjMwQpBXNJ4gNWXFy8LYSeLNianp5GVlYWlpeXoVAofHasSDHaCgYREa1Wi71790ruYYRlWfz0pz/Fj370I3z5y19Gd3c3Xn31VZSUlKCrqyvh0tiRILmIxP+LTFFUVLvUCaT7Kzc3l3PTLcxgYFlbF5JsLaBRAp3TCk7MounMGhwcxNzcHJqamrbFE6HL5UJ7ezvUajVaWlpCqh/wc821tbVcN9L4+Dh6eno2pGJizcLCAjo7O7eNDxj53BmNRhw8eBBpaWmcU/TCwgKGhobQ1dXFDZ/6mx1KEbfbjQsXLnDrqaUoIg8//DDuu+8+/PnPf0ZrayuA9dms8+fPvy9EBJBgRELT9IYurTNnzmD//v0RH8izs7Po6elBdXU1ysrKgk6q9/T0wOFwoKqqCunp6WF/cL1eL7q6urC2toaGhoaELTzzITWeQJb2kULMJU0mE5aWljijw7y8vJhMeZM5nu0SLfL3q5O6VSD40/XLy8uSNpgkIkLqVlIUkV//+tf49re/jT/96U+Cz7olEgkhJG+88QZ27doV9swFCfMnJyexf//+oF0rpB6yurqK0dFRn2G8vLw8pKVnYtVDQaMAUjfpCF1bW4PBYIBGo8G+ffskkbqJlsXFRRgMBlFrPMTo0GQywWKxcOaSeXl5othYTE5OYnh4GPv27ZP0HE+oRLpf3e12+0zXq1Qqn+n6eKbAEkFEHn30UXzjG9/AM888I+icWyIiOSEJtG737bffxs6dO8PyqCGRAflyBZvZIO29xPWXoihuGM9kMqF92o2+tRy41TpkpGnRVKzGB0pZpPMEZXl5GQaDAbm5uairq5PcBz8S5ufn0dPTg9raWhQXF8fkmmTKmzw1MwzjMwQZTUsu2SszPT2NxsZGZGZmCnjn8YGsOVhbW0Nzc3PEcy+B6lnEPiQ3Nzem8zRutxvnz59HWlqaZEXkD3/4A7761a/i5MmTuOqqq+J9S3EnIYTk3Llz3LBQKKytrXHb3hoaGoJ+CbbqzBq0UvhDnxI2hxsaegXLNieWPWrs1tO4aS+LgrwcLCwsoKenB5WVlSgtLZVUeiASyIbK0dHRqIwKhbgP0uJqNps5c0li2RJO/pmkfhYWFtDU1CT5YdBQIPvIvV4vmpqaBIuAyb4P8r4T+xD+dL1Yn3GXy4ULFy5wmzSlJiIAcOrUKXzpS1/C448/juuuuy7etyMJEkJILly4gNzc3JAKoktLS5xdx1b5fDJkGKyo/vseJXosClRlrb9NLFgs2VyYsLpxUcogdMwCWJblUj+Jns7ie0w1NjZKavEOye+bTCYf99ytDjeGYdDV1QW73Y6mpibJF5hDwePxwGAwgKIo0VfJulwubrqepHzJ+y5k6pGICFmyJcUHsj/96U/43Oc+h0cffRQ33nhjvG9HMkhOSMjedj4GgwEZGRlbWg2EU1QnkQiwuf27lwH+67waDAPkpvi+TYNWCg2aYeS5x1BQUIClpSXYbDbodDruiTnRDiySJiEHrhTW9G4G8UYymUywWq2cdUheXp7PtLHX6/VxJI615YkYkAn8eOxXD5R65O9YifRBKhFE5M9//jNuvvlm/OpXv8LHP/7xeN+OpEgIIenu7kZSUtKmdgORFNXJj72Ve+8vO1SYXqFQlvne27TmoWEYX8Jl2VM43lrFCYZ/JxKxY8/Ly5P8Xg5i4UKecBMpsuLXsxYWFgCAKxhPTEwgKSkpZrtexIYsXJNCOyx/r83CwgJsNhuysrI4UQn1M0/W/RLnBymKyKuvvopPfvKT+PnPf45Pf/rTkrzHeJIQQtLX1wcA2LVr14a/T4bkbDbblrnvQEX1rWibV+DJQSUyNIBey2J1zY2uyWUUpdH4p8t1SE0K/DRI7NjJE7NWq+VERWptlg6HA21tbdzTYKINrPEhcxNzc3OYm5sDsO5HlZeXJ9rOiVhBan+ZmZmCtWELydraGtcFZrVakZKS4rNEKtBnnohIVlYWdu/eLanvBeH111/HiRMn8N///d+45ZZbJHmP8UZyQgKsP3XxGRoagsvlwp49e3z+XMii+mbQDPDmtAJ/nVXCtOLC8uICqnI0+LuDWSgKcUUFsQ0hT8z89ladThfXDyaZ7C4sLNwWK1cBX1v74uJin6JxZmYml3pMpBmfRNuv7vV6uRUEJErkT9erVCo4nU6cP3+em0+S4s/09ttv4yMf+Qj+/d//HV/4whckeY9SICGEZHR0FKurq9i/fz/3Z4uLi2hvb0d+fj527doVdVF9K/rG5nC+fwo1leVorsqDKsKHQf7udJPJBAA+XlSxjAZMJhO6u7u3zWQ38N7cS1lZGSoqKnx+18SSnTwxp6amckVjKZtLkv3qhYWFqK6ulux9bkag7rvMzEzY7XZkZ2dj7969kvyZzp07h+PHj+OHP/whbrvtNkneo1SQpJD4r9udmJjAwsICmpubAQAzMzPo7e1FTU1N0HbbUIvqwSD1l5mZGezfv19QF1Wy44OICvGiImkYMXP6xBm2vr5+W+yoAN5b3lRTU7Pl3AtxcSVDkEqlkhMVKZlLbrf96sC62Hd0dICiKHg8HkkKeltbG66//np873vfw9e//nVJ3JOUSQghmZ6exuzsLFpaWjA4OIipqSk0NDQEnUoOt6geCH4XU0NDg6gFc7IZj+xOJ09rpK4iVG6fZVkMDw9jZmYGDQ0NyMrKEuR1483s7Cz6+voiWt5EhvHIe89fdRtPc8nttl8dWE9HX7hwAdnZ2di1axeX9iX/IW7RkW4mFILOzk5ce+21+MY3voFvfetbsoiEQEIIyfz8PEZHR5GcnBxyUZ3UQyiKiujp0ul0wmAwcPugY32Y+M9MkNx+Xl5exG25DMOgu7sbKysraGxslHwnWaiMj49jbGxMkIgxkKDHo6XbYrGgo6Nj2+xXB94TEb1ej7q6ug0HNGmUICkwspmQCEssDBB7e3txzTXX4Ctf+Qr++Z//WRaREJGkkPjvbZ+enkZvby90Oh32798vSlGdz8rKCgwGA1fYjHeag+T2TSYTFhcXkZaW5tNWHMrP6PF4uHmKxsbGhO5eIpC04+zsLJqamkQZniTb8cxmM2cuSURFLHNJs9mMzs5O7Nq1a1vsVwfWReT8+fPIzc1FbW3tlu8by7Lcw9TCwgKWl5eRnp7OiYoY7/3AwACuueYafP7zn8e9994ri0gYSF5IFhcXceHCBbAsi8svvzzooS6EiBiNJrzaPobUvHLUleWjUgcopZEuB7D+3pCDbWFhwcdY0n/tJ4Fs/9Nqtdi3b19Ct/cSyIItq9WKpqammERXfJND8t4LPeFN/M22y351IHwRCYS/wSTpfMzJyRGkpjU8PIxrrrkGn/rUp/Bv//ZvcX94TDQkLSSkqF5aWorp6WlcfvnlAf++UEX1vpEp/F8HiwV1KWhlMpKULGp0DG7e60WuBDtF+YN4ZrMZCoWCExWdTgeFQuHTCltbW7stviB8o8JQFmyJdQ+k+25hYd0mh7+0KxKxnpmZwcDAwLZxJQbea1vOy8sTrL2cpmnOYHJhYYEzmCTvfbjR9vj4OK6++mocO3YM//Vf/7UtviOxRrJC0tfXh+npaezfvx9arRZvvvlmQJdNMmRIIphIRIRhGPT39+OPQ1oMslUoyVQgXQOseYGpVQUa82ncfsALKUe6/IKxyWQCwzDIyMjA0tISysvLt03HD/GYAiCZCXx+e6vJZILT6Qw7t0+s7bfLfnVAHBHxJ5DBZGZmps90fbDrTk1N4aqrrsLVV1+NBx98UBaRCJGkkLS3t3PtvqmpqXC5XDh9+jSuvPJKn1+0EEV1j8eDzs5OLDlZPGM/BKVCiRyer5bNDSy6KPzTIQ8qsiT3VgWE2KWPjY1Bo9HA6/VCr9dzuX0pHL6RQOxByN5uqabo7HY7FyUSc0ny3gdKwY2NjWF8fBxNTU3bwtoeeG9DYH5+fkwHXZ1Op890fbD049zcHK666ipccskl+MUvfhG3z5PX68X3v/99/Pa3v8X8/DwKCwtxyy234Lvf/W7CCJskzYfKy8tRXV3NHXjkF0ysTQBh6iEOhwPt7e1ISUlBdf0+eP6qRJraVyy0KmDeAdg9FADpCwnLshgdHcXU1BSam5uh0+m4g21ycpJrWkg0Y0li40KsNKT8BUtNTUVFRQUqKio451yz2YyRkREkJydz731GRgZGR0cxPT2NAwcOID09RKsEiWO323HhwgUUFBTEfIAyOTkZxcXFKC4u5lK/ZL6IYRiuC/Lo0aO46aab0NraGlcRAYD7778fP/vZz/B///d/qK+vx/nz5/G5z30OmZmZuP322+N2X+EgSSFJT0/32dFOfslerxdqtVoQESFDUcQaxEVTyEpmseSkkMoTkyUXkK4G8lKZIK8mDUgB2mKxoKWlhWuRTktLQ1paGnbu3Im1tTWYTCbMz89jYGBgy6dlKbCyssLZuCTaZHdSUhJ3sPFtQ9rb27l0bE1NTULZtQQjniLij1Kp5GqGxGDysccew3/913/hjjvuQF5eHhoaGjAxMbGls7iYnD17FseOHcO1114LYP1B+ve//z3Onz8ft3sKF0k+1vl/+CiKglKphNfr5UwXoxGR2dlZtLW1oaqqiusiSVYBl5UycDHA1CqFFRcwZ6NgdSpwuIhGnsS/516vFwaDASsrKzh48OCmczZarRZlZWVoaWnBJZdcgh07dmBxcRFnz57F22+/jeHhYaysrEAqGU+r1YoLFy6grKws4b3AVCoV8vPzUV9fj9zcXKhUKhQUFGB8fByvvfYaOjo6MDs7u2EfT6Jgt9tx/vx5SQo+RVHIzMzEiRMnkJmZiSuuuAJ33303XnzxRdTV1eGWW26J270dPXoUr7zyCgYHBwEAHR0dePPNN/HhD384bvcULpKskQTa2/7qq6+ioaGBC/8j7cwaGRnB1NQU9u3bB71e7/PPGRZ4bVKBM5NKLDoppKiBo8U0rqygoZFmOh7Aeu2Av58iEmuVQMaS5GluM+dWsTEajdyq31C3Y0odMhRKBmuTk5O5gjGpq/Dt2KMZQI0lRER27NiBqqoqSYkIYWlpCddffz0KCwtx6tQprrtrZWUF8/PzqKmpict9sSyLu+++G/fffz+USiVomsa9996Lb3/723G5n0hICCFhWRavv/468vPzUVJSElFen6Zp9PT0YGVlBQ0NDUEn4900sOoGUtVAsiSTf+9hs9nQ3t7OOagKUTvgt7aazWYA4NJfer0+JvWJ6elpDA4OYs+ePcjLyxP9erGApml0dnbC5XKhqalp0zZVstfGbDZjcXGR86KS4goC4D1TyaKiIlRWVkru/oB1sTh+/DgyMzPx9NNPS6o2+Nhjj+Gb3/wmfvzjH6O+vh4GgwF33HEHfvKTn+Czn/1svG8vJCQpJPx1u6QeMjc3h6mpKayurnLF4ry8vJBaK10uFwwGAxQKxZaT8YmE1WpFR0cHt+ZXjC8w37YiFsaSLMtifHwc4+PjaGhogE6nE/T14wXZr06cBULtnPN4PD5DkGQQjyzuinfTQSKIiM1mw0c+8hFoNBr86U9/klw9qqSkBN/61rdw2223cX/2wx/+EI8++ij6+/vjeGehI+nnbZZluciksLAQO3bs2FAszszMRH5+PvLy8gI+ZayursJgMAj6xC4FyAR0bW3tlk630aBQKJCdnY3s7GzU1NRwPlSjo6Po6enhjCVzc3OjFmiWZTE4OIj5+flt1cXE36/e1NQUlviq1WoUFhaisLCQW0FgNpvR09MDmqZ9hiBjvQGSiEhxcTEqKytjeu1QcTgc+PjHPw6FQoFnnnlGciICrN+j/7mkVCp9bKKkjiQjEpqm4Xa7t9wh4nK5uAG8xcVFpKenc6KSkpLCtf2Vl5dv2E2RqLAsi4mJCYyOjsZ9Atp/XiIrK4sTlXDz+gzDoKenB8vLy2hqapLkFz4S3G432trauHW/QrWZ8tfcmkwmOBwOnyFIsVM3NpsN58+f56JhKeJ0OvGJT3wCdrsdf/7zn0XxYhOCW265BS+//DJ+/vOfo76+Hu3t7fjiF7+Iz3/+87j//vvjfXshIUkh+eUvf4nl5WXccMMNKCgoCCmKIKttjUYjrFYrNBoN3G43qqqqto0FN8uyGBgYgNFoRGNjo6S+GNEYS9I0jY6ODrjdbjQ2NsbE5TUWuFwuXLhwAampqaLvV3c4HJyo8w0OwzH2DJXV1VVcuHABpaWlcW2bDYbL5cJNN90Es9mMv/zlL5Jel7C6uorvfe97ePLJJ2EymbBjxw586lOfwj//8z8nTBpekkLy6KOP4qGHHsJf//pXHDp0CMeOHcOxY8dQXFy85ReCzFIYjUakp6djZWWF25een58vmmOr2PB3ozQ1NUm6k4cYS5KlUWQILy8vb8PiIrfb7VO/StSpe3+IZXo8BiiJwSF5/8l0txAdeIkgIm63GzfffDOmpqbw8ssvb+jOlBEeSQoJsP70PTs7i1OnTuHkyZN466230NjYyIlKoFSVx+NBV1cXXC4XGhoaoNVqfdpazWYzkpKSuPSXVLaxbQU5bCmKkoy/VKjQNM0Vi81mMzckRtJfBoMBqamp2LNnj2QtT8LFbrejra0NOTk5AfduxBL+dDfpwCPpL71eH9Z7TkSErDGWIh6PB3//93+PgYEBvPrqq8jNzY33Lb0vkKyQ8GFZFkajEU899RROnjyJ1157DfX19Th27BiOHz+O6upqDAwM4PHHH8f111+Pffv2BSw8ki+V0WjEwsICVCoVF6lsZsEeb4g1SEZGBurr6xP6sOUbSxqNRng8HqSkpKCqqipix1ypQQrQUpynIKudSbRIFkeF0ixBVv5KWUS8Xi++9KUvoaOjA6+++ioKCgrifUvvGxJCSPiwLAur1YqnnnoKp06dwssvv4yioiLMzs7igx/8IB5//PGQDiSGYXws2CmK4kRFqN0S0bK8vMxZgyT6VDef5eVltLW1cYcXOdT4bcWJFHURyO+rtLRU8s0d/MVRpFmCuOb62+WsrKzgwoULkl75S9M0vvKVr+Ds2bM4c+bMtlkIligknJDwYVkWv/zlL3HbbbehuroaIyMjKC0txbFjx3DjjTeGXOD0t2BnWZbL6QuxNCcSTCYTuru7UVVVhdLS0phfXyzICln+zxVospt0IIU6KxRvFhcXYTAYsHPnTpSVlcX7dsKGuOaaTCZYrVakpKRwgtLf34+dO3dKVkQYhsEdd9yB06dP4/Tp09vq+5IoJLSQPPjgg/j2t7+Nxx57DNdccw1WVlbw3HPP4dSpU3jhhReQl5fHpb+am5tDEgQS/huNRphMJtA0zR1o4eaUI2VqagpDQ0Oor6/fNlvygPdmX3bv3o3CwsJN/x6ZFTKZTFheXuaMJUlbt9Qg4lhTUyPqTE+sIOaSMzMzsFgsUCqVKCgoiOuD1WYwDIN/+qd/wnPPPYczZ85INu223UloIRkcHITb7caePXs2/DPSO37y5Ek899xzyMrKwg033IBjx47h0KFDIQkC6dUnouJ2u5GTk4P8/HxRcvosy2J4eBgzMzNoaGiQdMtiuBBxDHf2hdiwkyfl1NRUTlSk0IFnMpnQ1dW1pTgmGiT9WFFRgfT0dC4FRrYRSiEFyTAMvvOd7+DkyZM4ffo0qqur43Yv73cSWkhCZW1tDX/5y19w8uRJPPvss0hOTsb111+PG2+8EUeOHAlpIphlWW6q22g0wul0CprTJ2Z+KysraGxslKyle7jw96NEK47+diEajUawttZImJubQ29vL/bu3btt/MCA90SksrLSJ00UKAWp0+m4ukosW9JZlsUPfvAD/OY3v8Hp06dRV1cXs2vLbOR9ISR83G43Xn31VTzxxBN4+umnQVEUrrvuOtx444245JJLQhIEUqgkkYrdbvfZQBjuEJHH40FHRwfnw5QoQ0hbwbIs+vv7YTab0dTUFNQoM1z8jSUpiuJEJRbpF2IqGW93AaFZWlpCe3v7BhEJhL+5ZFpaGicqYppLsiyL++67Dw8//DBeffXVgBkJmdjyvhMSPl6vF6+99hqeeOIJPPXUU3C73bj22mtx/PhxXHbZZSEXeYlViMlkCttUcm1tDe3t7dBqtZJeHxsuJMJaXV0VfYCSGEsSUfF6vVy0qNfrBfegIhY128lUEnhPRKqqqlBSUhLWv0uiRTIEKZa5JMuy+MlPfoIHHngAr7zyChoaGgR5XZnoeF8LCR+apvHmm2/i5MmTePLJJ7G6uoprrrkGx48fx4c+9KGQD0JSKDYajVxL5Wamkqurq2hra0NeXh5qa2slVcSMBq/Xi46ODni93phHWP4eVGtra4IZS7Isi7GxMUxOTqKxsXHb7FcHohMRf0i0SKIVhmEEEXaWZfE///M/+Ld/+ze8+OKLaGlpieo+ZYRDFpIAMAyDd955hxMVs9mMq666CseOHcNVV10VcoqG+E8ZjUYsLS1x3Uf5+fmw2+3o7OzkevPjXTQWCmJSGM2SLSHxjxaJseRmbtGbQRohZmdn0dTUtG2ciYH11uX29nZRus6IsJNokZhLEmEPNepnWRY///nPcc899+CFF15Aa2uroPcpEx2ykGwBwzBoa2vDE088gVOnTmF6ehpXXHEFjh07hmuuuSZkmxW3280daBaLBcD6sqiqqqptU1hfW1tDW1sb0tPTsWfPHslFWE6nk/sdLC0tIT09nTvQgj0cELNMk8mE5ubmbfP7AsQVkUDwhyBJazd/CDLQd4llWTzyyCO4++678dxzz+Hiiy8W/T6DMTMzg7vuugsvvPAC1tbWUFNTg1/+8pdobm6O633FE1lIwoBhGHR1deGJJ57Ak08+ieHhYXzwgx/EsWPHcO2110Kn0wUVFdLBNDExgZKSEthsNlitVmi1Wi79JYWW1kiw2Ww+aTqp/wz+xoZarZYr1vMfDliWRW9vLxYXF9Hc3Cxps8xwsVqtMBgMcZt/IY7dpLU7OTmZExXShceyLH7zm9/gm9/8Jp555hlcdtllMb9PPouLi2hsbMRll12Gf/zHf0ReXh5GRkZQXl4uWTv9WCALSYSwLIu+vj5OVHp6evCBD3wAx48fx3XXXYecnByfw5S4ElssFp8OJmIqSfy/gjnlShWSXyeOsIlwz3zIAB7ZV0+MJXNycjAzM8M5LktpPWu0EBGpra1FUVFRvG9ng7nkY489hsXFRRQUFOB3v/sdnnzySVx55ZXxvk1861vfwltvvYU33ngj3rciKWQhEQCSPyei0t7ejosuugjHjx/HDTfcAI1Gg1tvvRU333wzPvjBD256IBGnXJJPVqvVkjeVJMvDqquroy7SSgGyhdBoNGJubg7AegqyoKAgZs4GYiM1EfGHYRi89tpruP/++/Hmm29Co9Hg6quvxrFjx3DdddfF1dF39+7duOqqqzA9PY3XXnsNRUVFuPXWW/GFL3whbvckBWQhERiywfDkyZM4deoU3nnnHSQlJSEnJwdPPPEE6uvrQxIEvqmkyWTinpLz8vK2TKHFitnZWfT19WHPnj3bysqF33VWWVnJzau43W7JTHVHCrFzqaurk7Sx4bPPPovPf/7zePTRR1FdXY2nn34aTz/9NJqbm/HQQw/F7b7IQ+Cdd96JEydO4Ny5c7jjjjvw85//HDfffHPc7iveyEIiIj09PbjqqquwY8cOJCUl4Z133kFTUxO3UyXUbi1iKmk0GmE2mzlTyfz8fEF79MNhYmICIyMj2L9//7ZaHOTxeNDe3g6lUunTdcaf6iZDqJF0H8WTRBGRF154ATfffDN+/etf48SJEz7/jKbpuEaFGo0GBw4cwNtvv8392de+9jW8++67OHv2bNzuK97EtzdzGzMzM4OjR4/ia1/7Gr7//e8DAIxGI5588kmcPHkS3//+97Fnzx5OVKqrqzcVFYVCAb1eD71eD5ZlOafinp4ezlQyPz8f2dnZon/J+H5gzc3N22qWIth+dYqikJ6ejvT0dFRWVnKrbWdnZ9Hf389ZsEvdWHLXrl2S9gR75ZVX8NnPfhYPP/wwPvaxj2345/FOLRYWFmL37t0+f7Zr1y6cPHkyTnckDeSIRETeeecdHD58eMOfsywLi8WCp59+GidPnsQrr7yCmpoazql4165dIUUqLMtieXnZZ1EUOczEMJUkDQNWqxVNTU3bqg3W6XSira0NaWlpYbcuS91YcmFhAZ2dnZIXkddffx0nTpzA//zP/+Czn/1s3N+3QHz605/G1NSUT7H961//Ov7617/6RCnvN2QhiTPEtv6ZZ57BqVOn8NJLL6GsrIzbqRLqoUZMJYn/F99UMjc3N+rBQLIz3uFwbLsOJrJfXafTYffu3VEdYHyrkIWFBSQlJXGiEo+GCSIiu3fvlvTGwLfeegsf/ehH8e///u/4whe+IEkRAYB3330XR44cwQ9+8AN8/OMfx7lz5/CFL3wBDz/8MG666aZ4317ckIVEYqysrOBPf/oTTp06hT//+c/Iz8/nIpWmpqaQRSWQqWR+fj5yc3PDLhJ7PB4YDAawLIvGxsaELDJvht1ux4ULF0SZfyEtrURUYm0sSTrqpC4i586dw7Fjx3Dvvffitttuk6yIEP70pz/h29/+NoaGhlBRUYE777xT7tqShUS62O12vPDCC9xOFZ1OhxtuuAHHjx/HwYMHQ05dEZsQo9HIbR8kT8lbeU+5XC60tbUhOTl5W5lKAu95ncVivzrfWJIsTOOvIRD6fSUiIvXlaG1tbbj++uvxve99D1//+tclLyIygZGFJEFYW1vDSy+9hJMnT+JPf/oTtFotrr/+ehw/fjzknSrkdUiksrKyEtR7yuFwoK2tDVlZWdi9e7fkLE+igezcKC8vj/lWPb7/FElD8tcQRBvxmc1mdHZ2Sr4tu7OzEx/+8IfxT//0T7jrrrtkEUlgZCFJQNxuN15++WWcPHkSzzzzDBQKBbdT5eKLLw75IPL3nuKbSnq9XrS1taGgoAA1NTXb6ktO9quHsnMjFvDbivnLosI1lgTe29godRHp7e3F1Vdfja997Wv43ve+t60+X+9HZCFJcDwej89OFY/Hg+uuuw7Hjh0La6dKIFNJvV6PmpoaQRdSxRtSfJbqVDdZFuVvLJmXl7dllxwREalvbOzv78c111yDf/iHf8APf/hDWUS2AbKQbCNomsYbb7zB2d/bbDZ8+MMfxvHjx3H55ZeHZDhIDqOCggK43W5YLBaunTU/P39Th9ZEwGQyobu7W/JtsAR/U0OtVsuJiv8GwkQRkeHhYVx99dW46aabcP/992+rdOn7GckKyYMPPogf//jHmJubQ319PR544IG420cnEjRN++xUWVhY4PyKrrrqqoBPtzMzMxgYGMCePXu4w8jr9XKHGd9UMj8/X9R1qkIzNzfH2blI+aDdDGLuSX4PZANhXl4e3G43enp6JC8iY2Nj3LK4Bx54QBaRbYQkheTxxx/HZz7zGTz44IO46KKL8POf/xz/+7//i97eXknktBMNhmFw4cIFzlRyZmbGZ6dKWloa7r33XlRVVeGqq65CdnZ2wNchppLEqVitVnP291I1lQTe26++XexciLGkyWTC/Pw8aJpGdnY2SktLY+JuEAmTk5O4+uqrcfXVV+PBBx+URWSbIUkhOXToEJqamnzM2Xbt2oXjx4/jvvvui+OdJT4Mw6Czs9Nnp0p+fj4sFgt+97vf4UMf+lBIgkDWqRL/LymaSgLbd786AMzPz6O7uxuVlZVcjcvj8fi0Fcd7QyWwbu559dVX4wMf+AAefvhhSQqdTHRITkjcbjdSUlLwxz/+ETfeeCP357fffjsMBgNee+21ON7d9sLtduPEiRN44403kJubi7GxMVx66aXcThW9Xh+yqSR5QjaZTNzgXTxNJbfzfnVgXUR6e3uxb98+5OTkAHjPWJKIO99YMpSZIbHu85prrsHBgwfx61//+n0rIizLSubhSgzi/7jix8LCAmia3tC6mJ+fj/n5+Tjd1faDYRh85CMfwfT0NHp6elBQUMDtVHnkkUdw++234+jRozh27BhuuOEG5OfnBzWVzMnJQU5ODurq6rjBu+7ubrAsy+Xy9Xp9TESFZVkMDQ1hbm4OBw4c2FZdZ8B79R6+iAC+xpJVVVUBjSWJqMRi06PZbMb111+PxsZGPPLII+9bEWEYhvvcz83NJUSjR7hILiKZnZ1FUVER3n77bbS2tnJ/fu+99+I3v/kN+vv743h324vHHnsMV199NbKysnz+nGVZjI+PcztVzp07h9bWVtxwww04duwYioqKwjKVJAOQXq9X1Glucs3+/n4sLCxsO2NJ4D0RCbfe43Q6uaaJxcVFpKWl+bQVC/20bLFYcO2116KqqgqPP/74trLVCQd+JPLRj34UOp0Od911F6qrq+N8Z8IiOSGRU1vSgmVZTE9P49SpUzh16hTefvttNDc3c/b3ZWVlIYsKmeY2Go1wuVzIyclBfn6+YLl8hmHQ29uLpaWlbbdfHYhcRPzxeDzcSlsxjCWXlpZw3XXXoaioCCdPnoxLSk1qfPGLX8Tjjz+O9PR0fPSjH8Wtt96K2traeN+WYEhOSID1YntzczMefPBB7s92796NY8eOycX2OMKyLObn57mdKq+//jr27dvHiUqoflX8JVFGoxFra2vIzs6O2FQSWBeRrq4u2O12NDc3J8SiqXAg6SmhO8/4xpJmsxkKhcKnaSLcVOTKygqOHTsGnU6Hp556alu5REeKwWDAN77xDdxxxx1YXl7GXXfdheuvvx5f+9rXsGvXrnjfniBIUkhI++/PfvYztLa24uGHH8YvfvEL9PT0oKysLN63J4N1MVhYWOB2qrz66quora3lRCXUnSpAdKaSwPph2NnZCZfLhaampm33BExEpKGhYdPWbCEgmziJqJClaaS+tVUq0maz4cYbb0RycjLnBycV7rvvPtx99924/fbb8cADD8T02ouLi3j99ddx+PBh5Ofn449//CPuvPNOXHPNNfja176GPXv2xPR+xECSQgKsDyT+27/9G+bm5rBnzx7853/+Jy655JJ435ZMAMhOlaeffhqnTp3CX/7yF5SXl3P29+EsiiIFYr6pJIlUAj3der1ezuK+oaFh2+XiyZCo2CLiT7jGkg6HAx/96EcBAM8995ykGhzeffddfPzjH0dGRgYuu+yymAsJ8N6KYFJ4f+aZZ/CVr3wFV1xxBe644w7s3bsXzz77LGiaxvHjx2N+f9EiWSGRSVyWl5d9dqoUFhbihhtuwI033ojGxsaQRYWYShqNRiwvL2/oOuLvV29oaNh2XUHxEhF/yH4bf2NJh8OB4uJiFBQU4BOf+AQcDgf+/Oc/IyMjI2736o/NZkNTUxMefPBB/PCHP0RDQ0NchITAL74///zz+Md//Ed86EMfQkVFBe655x788pe/xGc+85m43V+kyEIiIyo2m43bqfL8888jOzsb119/PW688Ua0tLSEfPi7XC7uICNdRy6XC6mpqWhsbNx2IkKm8RsbGyU3SLm2tgaTyYT7778fv/nNb5CSkoKsrCw89dRTaG5ujvft+fDZz34W2dnZ+M///E9ceumlcRcSwFdMXn31Vdx4441YXV3Fv//7v+POO++M671FiuxTICMqaWlpOHHiBB577DHMz8/jgQcewNLSEj760Y9i165d+H//7//hjTfegNfrDfo6SUlJKCkpQXNzMw4dOgSXywVgvUPo3LlzGBkZgc1mi8WPJDpSFhEA0Gq1KCsrwwMPPIAPfvCDyMzMxK5du3DkyBHs2bMH//3f/x3vWwSw3t7e1tYmuQYdfu2QYRg4HA789Kc/TVgRASQ4kCh17rvvPpw6dQr9/f3QarU4cuQI7r///m3VyicWKSkpOH78OI4fPw6n04lXXnkFp06dwk033QSlUslFKkePHt201rG2toaOjg7k5ORg9+7dPmaG4+PjQR1yE4GpqSkMDQ1JVkQIHo8Hf//3f4/5+XkYDAbk5ORgZWUFL7zwAify8WRqagq33347XnrppZh1jpE6iP//3oyFhQXcfPPNuOOOO3DrrbfG4hZFQ05thcnVV1+NT37yk2hpaYHX68V3vvMddHV1obe3d9sNv8UKj8eDM2fO4OTJk3jqqafg9Xq5nSqXXnop18o7MDCA2dlZFBYWBtyv7vV6YbFYOFNJjUbDORVnZGRIXlSmpqYwPDyMxsbGDUOiUsLr9eKLX/wiOjs7cfr0aUku0Hrqqadw4403+hzmNE2DoigoFAq4XC5B06F84fjud7+L0tJS3HLLLVt2EI6NjcV8Q6cYyEISJWazGXl5eXjttdfkrjIB8Hq9ePPNN7lFXTabDddeey1qa2vxH//xH/iXf/kXfOlLX9pSFPznI4ipZH5+PrKysiQnKokiIjRN47bbbsM777yDM2fOYMeOHfG+pYCsrq5iYmLC588+97nPoa6uDnfddZegLbf8msdVV12FxcVFfP/738fRo0e5xgO+Tcp2RBaSKBkeHkZ1dTW33lRGOGiaxtmzZ/HTn/4Uf/zjH6FUKnH8+HEcO3YMV155ZcgRIDGVJGaGFEVFNXQnNJOTkxgZGZG8iDAMg9tvvx1nzpzB6dOnE26lg9jF9jvuuAOvv/46XnrpJc4DbXFxESqVCunp6dtaTLbnTxUjWJbFnXfeiaNHj8oiIgJKpRJerxfPP/88HnjgAbz55psoLy/H97//fZSXl+Omm27CH/7wB6yurgZ9HWIqWV9fj0suuQR79+4FAHR3d+P1119HT08PzGYzGIaJxY/lAxGRpqYmyYvIN7/5Tbzyyit4+eWXE05EhISmaVx33XUYGhri/szlcmFwcBCf/OQnkZOTg9dffx333HMPmpub0dzcjHPnzm1bEQHkiCQqbrvtNjz33HN48803UVxcHO/b2XawLIvrrrsOH/vYx/C5z32O+3OGYdDR0cHtVBkbG8Pll1+OY8eO4dprrw3ZL4oMUpK2YmIqmZ+fH9Ikd7SQXSlNTU2StrlnGAZ33303Tp06hTNnzqCqqiretxRX3G43/vEf/xG//OUvAbyXtvr85z+P/v5+7N69Gx0dHaisrMTFF1+MF198EZOTk/jrX/+67ax7CLKQRMhXv/pVPPXUU3j99de3RbFMqmyVDmBZFr29vXjiiSdw6tQp9PX14bLLLsPx48dx7bXXhrxTxd9U0u12i7ogKlFEhGVZfP/738ejjz6K06dPo66uLt63FFf8u7HuueceXHLJJbj00kvx6quv4qmnnsLZs2fxrW99Cy0tLSgtLcVPfvITnDt3Dr/97W+33bwTQRaSMGFZFl/96lfx5JNP4syZM9vODjqRIXtIiKh0dHTg4osv5naq5OXlhWUqSezv19bWgtqDhMv4+DjGxsbQ3NwsqSlwf1iWxX333YeHH34Yp0+fRn19fbxvKe6QBxvy32VlZVAqlfjtb3/Lrb1YW1vjfMZmZmZw+eWX48Ybb5TcPIuQyEISJrfeeit+97vf4emnn/aZHcnMzJSUSd37HbIhkexUeffdd3HkyBFup8qOHTtC7twiTsXEHoTvVByuQeT4+DjGx8fR1NQkeRH5j//4D/zXf/0XXn31Vezfvz/etyQp2tra0NTUBAC45JJLMDExgf/7v//DxRdfDKVSiZmZGZw+fRr33Xcfdu/ejT/+8Y9xvmNxkYUkTDY7fB555BHccsstsb0ZmZBgWRZTU1PcTpWzZ8/iwIEDuOGGG3D8+HGUlpaGLCrEVNJoNGJ1dRU6nY7rANsq/z02NoaJiYmEEJH//u//xo9//GO89NJLOHDgQLxvSVI88sgjePjhh3H27Fnuzy699FKMjIzg17/+NT74wQ+iu7sbv/71r5Gamop77rknjncbG2QhkXlfwbIs5ubm8OSTT+LUqVPcThXSVlxZWRmyqGxlKsmHiEhzczPS09PF+NEEgWVZ/OxnP8O//uu/4s9//jMOHz4c71uSHA8//DB+8IMfYGRkBGq1mqt7XHHFFejr68MjjzyCK664gvtMvB+QhUTmfQvZqUJE5dVXX0VdXR0nKnV1dSGLir+pZHp6OvLz85GXl4f5+XlMTk4mhIj86le/wne+8x08//zzOHr0aLxvKe7whw0J/f39uP7663HmzBkUFRX51ESuu+46PP/887hw4QIaGxvjcctxQRYSGRmsHxiLi4t45plncPLkSfzlL3/Bzp07uZ0q9fX1Ic8BuN1umM1mGI1GWCwWAEBxcTFKSkoktaeDD8uy+M1vfoNvfvObePbZZ3HppZfG+5YkxUMPPQSr1YqysjLk5eXhM5/5DB566CF85CMf2fB37733XnznO9+Jw13GD1lIZGQCQHaqnDx5Ei+++CJ27NjB7VRpaGgISVRGRkYwOTmJ8vJyLC8vw2KxcKaS+fn5SEtLk4RVC8uyeOyxx3D77bfjySefxBVXXBHvW5IUMzMz+Pa3v42enh6srKxAq9Wiu7sb+fn5OHr0KMrLy3HkyBEsLy+/b+ukspBsc+K5YnS7YLPZ8Pzzz3M7VXJycnx2qviLCsuyGB0dxfT0NJqbm7kohO9UTEwlSfornqaSJ0+exJe//GX84Q9/wLXXXhuXe5AC/jNLm80w9fT04Bvf+AZSU1ORk5MDo9GI3t5e1NTU4Nlnn43lLUsGWUi2MVJYMbrdcDgcePHFF3Hy5Ek899xzSE1N5bq/WltbQVEU7rvvPuzfvx+XXnrppqksf1NJlUrFFepjaSr5zDPP4O///u/x29/+NiFXvArN+fPn8corr+DWW2+FVquFSqUCy7JgWRYMw3CDqV//+tfR2dmJl19+GRRFwWq1xnWLZbyRhWSbIrUVo9sRp9OJl19+GadOncLTTz8NlUqFnJwcTE1N4bnnngt5WyDDMD6iEitTyeeffx6f/exn8etf/xonTpwQ5RqJxPLyMmpra2EymXDgwAF84AMfwIc//GFcdtll3N9xu93QaDR48skn8YMf/ADvvvtu1AOq2wFZSLYpUlwxup1xu924+eab8eyzzyIpKQkKhQLXXXcdjh8/jksvvTTkwUWGYbC4uMh1gLEsi9zcXOTn5yM7O1swUXn55Zfx6U9/+v9v796DoqrbOIB/Ybml5gUQ8JYsCKEhRBCGlowViJkuLEMFgYZWw8UhLIvViNLyOimZorUIDI2DEJA24GhiIRAoEoKwEETIRUF2hxBduSW7v/ePhn3fffV9RVDOAZ7PP84clt3nzDB+95zf7zwPpFIpAgICHsp7jnYqlQrR0dGwtbXF1KlTkZ+fj2+//RZr1qzB008/jZCQEM1r6+vr4eDggLy8PHrOBtT9d0zi64jRsYoxhpiYGBQUFKCsrAwKhQIZGRmYOHEiwsPDIRQK8e677yI7Oxu9vb3/9710dXVhYmKC+fPnY+nSpXB0dISenh6qq6uRl5cHmUwGhUIBlUo15Hrz8vIQEBCAuLg4+Pv7D/l9xhqBQAA7Ozt8/PHHcHV1RWxsLMrLyzFlyhSEhYXB3d0dBw4cQHV1NfT19eHp6QkHBweuy+YFuiIZY65evQoXFxecOXNG09aCrkgerYHGhoGBgXf1XlOpVCgqKkJmZiaOHz+Ozs5OLF++HN7e3vD09MSECRMG/Rm3bt3S9P8aaCo50Kl4sE0lCwsL4evri7179+Ltt9/mfNcYH0dXr1mzBpMnT8bBgwcBAMuWLUNfXx8WLFiAhoYG5ObmIjU1FWKx+KE38xytKEjGmJEeMUoGT61Wo6SkRNP+vq2tDR4eHvD29oaXl9egH1ZkjEGpVGpufw00lTQ3N4epqen/vGdfXFwMb29vbN++HeHh4ZyHCMCv0dUDDx9KpVKkp6cjJycHYrEYFRUVyMvLw6xZs9DY2IizZ8/C19cX06ZNG9H6+IyCZIwZyRGjZOjUajXKy8s1odLY2IiXX34ZIpEIr7zyyqBnqgD/biopl8vR1dWl1al4YG2mtLQUq1evRkxMDCIjI3kRIvfCh9HVjDE4OjqipqYGc+bMwenTp6nL931QkIwDdGuL3xhjqKqq0rS/r62t1ZqpYmxs/EBNJQduf2VnZyM/Px/Ozs5ITU3F5s2b8dFHH/E2RADuR1cPPDvy/fffIzo6GnFxcfSA5iDQYjshHNPR0YG9vT0+++wzXL58GRUVFVi6dCni4+NhZWWF1atX48iRI5DL5bjf974JEyZAKBRi0aJFCA4OhoODA44cOQKlUomTJ0/i66+/xtWrV0fozB4MH0ZXD+yKc3V1RW9vLy5dusRJHaMNXZEQwlMDT8gPzFQpLS2Fm5ubZlDX/Waq1NTUYMWKFXjnnXcQFhaGEydOICMjAzKZDK2trbxbKObb6GqpVIpNmzbh3Llzmtkj5N4oSAgZBRhjaG5u1pqp8uyzz0IkEkEkEt01U6Wurg4rVqzAm2++id27d2s9f9Ld3T3o3WIjhY+jq5uamvDWW2/h2LFjsLCw4LocXqMgIWSUYYyhtbVV0/6+oKAAjo6Omvb3urq68PLyglgsRmxs7CN7Mv5h4Pvo6t7eXhgZGXFdBu9RkBAyijHGoFAocOLECc1MFV1dXQQEBCAhIYHXIQLQ6OqxgoKEcKKlpQVRUVE4deoUenp6YGtri4SEhEH3pyJ3Y4yho6MD27Ztw969e3m3BnIvNLp6bKAgISPuxo0bcHJywrJlyxAaGgozMzPU19fD0tIS1tbWXJdHCHlAFCQ8plaroaOjw+t9/0MhkUhQWFiIgoICrkshhDwE/L6BOk6p1WrI5XLo6uqOuRAB/pmB4eLiAj8/P5iZmcHJyQnx8fFcl0UIGSIKEp7p6+tDUlISZsyYgZCQEKjVaq5LeuiuXLmCw4cPw8bGBj/99BNCQkIQERGB7777juvSCCFDQLe2eEYqleLnn3+GSqVCa2srioqKNM3kxgoDAwO4uLigqKhIcywiIgIlJSU4f/48h5URQoaCrkh4YCDLL1y4gP3790MkEmH58uWwtbVFT0/PXSEy2rN/xowZWLBggdax+fPno7m5maOKCCHDQUHCAzo6OpDL5di8eTOWLFmC119/HTY2NqipqUFPT4/mdVeuXMGtW7ego6OjFSYqlUoz6KimpgZZWVkjfg4PYsmSJaitrdU69scff2Du3LkcVUQIGQ4KEh7o7OyERCKBkZERpFIpBAIBVCoVGhsbYWxsrAmNvLw8CIVCTZgMEAgEmhkjxcXFOHjwINrb2zk5l8HYuHEjLly4gB07duDPP/9ESkoKpFIpwsPDuS6NEDIUjHAuIiKCWVhYsEmTJrGtW7eya9eusZycHCYSiVhDQ4PmdQqFgllZWbHMzEzGGGM3b95kEomEJScn/8/3VqvVj7r8IcnKymL29vbM0NCQ2dnZMalUynVJhJAhosV2jp08eRJ+fn44cOAAurq6kJaWht9++w2GhoYwMjJCYWEhbGxscOfOHejr68PHxwcmJiYIDg5GTEwMmpub8cEHHyAkJAQymQxnzpzBhg0bNAONgH/WVFQqFQQCwZhatCeE8AMFCYcqKyvh6emJV199VfMchUqlQnt7O5KSkrBv3z7U19drjWBNSUnBnj17cO3aNbz22mvYtGkTrKysAABr167FL7/8gpKSElhYWKC3txdKpRLTp0/n5PwIIeMDrZFwyNLSEnFxcYiJiQHwz4OIAoEA5ubmcHFxgVAoxOXLlzWvr6urQ2ZmJioqKhAYGIhDhw5pQgQAzp49i/Xr18PCwgK///47IiMj4e7uDisrK0gkkgcaaKRWq0f97jBCyMigIOHQ448/DrFYjDlz5gCAVqfW2bNno7q6WrMbKyMjA2KxGADw3HPPwdDQEMA/ba4BoKKiAtevX4eHhwcYY9i/fz/y8vJw+PBhbN26FWVlZfj000/vuwjf0tIClUo1Zp+qJ4Q8fBQkPGVqaopFixZh8uTJCAsLQ1BQEHx8fJCYmAiRSITz58+js7NTEyhJSUmwt7eHtbU1dHR0YGRkhNmzZ8Pd3R1BQUE4duwYvL29YWpqes/PUyqViIiIgJ+fH6ZOnQovLy9UVlaO5CnzTn9/P6KjoyEUCvHYY4/BysoK27ZtG5PdBggZFg4X+skg9Pf3s/T0dK2dWXV1dczAwIA1NTVpjs2aNYtt2bKF9fX1McYYO336NLO2tmYikYjl5+f/38/o6elhwcHBbPr06SwxMZEVFxczX19fFhAQwLq6uh7NiY0CX3zxBTMxMWHZ2dmsoaGBpaens0mTJrGvvvqK69JGXFxcHLO0tGSGhobsmWeeue/fFBlfKEhGoRs3brAXX3yRrV+/njHGWFVVFdPX12fnzp3Tel1lZSULDQ1lzs7OLDc3lzGmvR24v7+fMcbY0aNH2bx581hKSormZ7m5uUxPT49VVVU94rPhr5UrV7J169ZpHROLxSwwMJCjiriRmprK9PX1WXx8PKuurmbvvfcemzhxotYXGTK+UZCMUnfu3GE1NTWMMcaioqKYiYkJ++uvv1hjYyNLSEhg7e3tjDHGOjo6WFBQEHNzc7vrPQaC5Pnnn2d+fn5MLpdrfiaTyZizszNLTEwcgbPhp507d7K5c+ey2tpaxhhj5eXlzMzMTCtwxwNXV1cWEhKidczOzo5JJBKOKiJ8Q2sko5Senh6efPJJMMawa9cu/PrrrzA2NkZHRwd++OEHvP/++7h48SJaWlrQ1tamWbT/z/v7AoEAPT09uHjxIlauXKm1TfjmzZuQy+WwsLAAMPr7ew1FVFQU/P39YWdnB319fTg5OSEyMhL+/v5clzZi/v77b5SWlsLT01PruKenp1bTTTK+UZCMcgM7q+zs7AAATk5O2LJlC7q7u+Hl5QVfX18YGhpi165dWr83ECg5OTmYOXMmbGxsNO+lVqshk8nQ3t4ODw8Prc8ZT9LS0nD06FGkpKTg0qVLSE5Oxpdffonk5GSuSxsx7e3tUKlUMDc31zpubm6OtrY2jqoifMP/oc7kgS1evBiLFy8GAMhkMsybNw9GRkYA/r3FeODf27dvY9q0aZgyZYrm99va2nDq1Cm88MIL0NPTg1qt1tqaPF58+OGHkEgkeOONNwAACxcuRFNTE3bu3Im1a9dyXN3IulcH6vH45YLc2/j732Gcsbe314TIvbi4uKCiogKNjY2aY1lZWZDJZAgNDQUwPq9GAKC7u/uuABUIBONq+6+pqSkEAsFdVx8KheKuqxQyftEVyTgnFAoREhKC3bt34/bt22hoaMDnn3+ODRs2YNWqVQDGb5CsWrUK27dvxxNPPIGnnnoKZWVl2LdvH9atW8d1aSPGwMAAzs7OyMnJgY+Pj+Z4Tk4ORCIRh5URPqFeWwQtLS3YsWMHfvzxRyxcuBAvvfQSNm7cqGlNP14plUp88sknOH78OBQKBWbOnAl/f3/ExMRoNcUc69LS0hAUFIRvvvkGbm5ukEqliI+PR1VVFc2QIQAoSMh/USqVWk0iCQGAQ4cOYc+ePbh+/Trs7e0RGxuLpUuXcl0W4QkKEkIIIcNCi+2EEEKGhYKEEELIsFCQEEIIGRYKEkIIIcNCQUIIIWRYKEgIIYQMCwUJIYSQYaEgIYQQMiwUJIQQQoaFgoQQQsiwUJAQQggZFgoSQgghw/IvDQEn8pDiMFkAAAAASUVORK5CYII="
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"N_CLUSTERS = 5\n",
|
|
"X = generate_synthetic_data(17, 3, N_CLUSTERS, random_state=0)\n",
|
|
"labels, _ = k_means(X, N_CLUSTERS, 0.001, n_init=5, random_state=2)\n",
|
|
"visualise_clusters(X, labels)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"images/kmeans_result_annotated.png\" width=\"300\">\n",
|
|
"\n",
|
|
"The diagram is the expected result after running the test case above. Note that points that belong to the same cluster are of the same colour. Moreover, in this diagram, for clarity, red circles have been drawn in to demarcate the different clusters as well. The diagram generated by the test case will not have the red circles."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Part 1.2: Performing Compression"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 1.2.1: Compress Image\n",
|
|
"\n",
|
|
"Now that we have done the hard work of implementing the K-Means algorithm, let us use it to compress images using the method described in 'Overview of Compression Procedure'."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Specifying Image File Path\n",
|
|
"\n",
|
|
"`IMAGE_FILE_PATH` specifies the file path of the image that is to be compressed,\n",
|
|
"relative to this notebook. Its default value is set to 'images/teddy_bear.jpg'.\n",
|
|
"However, please feel free to update it with your own image."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 84,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:39.347845Z",
|
|
"start_time": "2024-04-13T11:07:39.345967Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"IMAGE_FILE_PATH = 'images/teddy_bear.jpg'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Displaying Image Before Compression\n",
|
|
"\n",
|
|
"Let us have a look at the image before compression."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 85,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:39.521211Z",
|
|
"start_time": "2024-04-13T11:07:39.384919Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"display_image(load_rgb_image(IMAGE_FILE_PATH))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Defining the Image Compression Algorithm\n",
|
|
"\n",
|
|
"The final task of part 1 of this problem set is to implement `compress_image`, which returns the result of the image after it has been compressed.\n",
|
|
"\n",
|
|
"To be specific, this function accepts `image`, `n_colours`, `threshold`, `n_init` and `random_state` as inputs. \n",
|
|
"- `image` is a 3D array that represents the image to be compressed, with **integral entries** between 0 and 255, inclusive; \n",
|
|
"- `n_colours` is the number of colours that the compressed image should contain; \n",
|
|
"- `threshold` is a positive numerical value that determines the termination condition of the K-Means algorithm; \n",
|
|
"- `n_init` specifies the number of times to run the K-Means algorithm before selecting the best centroids and cluster assignments for compressing the image; and \n",
|
|
"- `random_state` determines the random state of the K-Means algorithm.\n",
|
|
"\n",
|
|
"It then returns a 3D array which represents the compressed image after recolouring every pixel to one of the `n_colours` colours that were 'picked' by the K-Means algorithm (i.e. the centroids of the clusters returned by the algorithm). As with the input `image`, the returned 3D array should have integral entries between 0 and 255, inclusive.\n",
|
|
"\n",
|
|
"\n",
|
|
"**IMPORTANT**: you **MUST** call `k_means` with the given `random_state` and `threshold`. This allows us to easily reproduce the result which you have obtained whenever necessary.\n",
|
|
"\n",
|
|
"**NOTE**: It is possible for `compress_image` to take some time to complete running, especially for bigger images and larger values of `n_colours`. As long as your solution does not cause the test cases for task 1.1.6 to fail on Coursemology, you can assume that your solution is reasonably efficient."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 86,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:39.525077Z",
|
|
"start_time": "2024-04-13T11:07:39.522287Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def compress_image(image, n_colours, threshold, n_init=1, random_state=None):\n",
|
|
" '''\n",
|
|
" Compresses the given image by reducing the number of colours in the image to\n",
|
|
" `n_colours`. The `n_colours` colours should be selected using `k_means`.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" image: np.darray\n",
|
|
" The image to be compressed. It should be an `h * w * 3` array, where `h` and\n",
|
|
" `w` are its height and width of, with integer entries.\n",
|
|
" n_colours: int\n",
|
|
" No. of colours that the compressed image should contain.\n",
|
|
" threshold: double\n",
|
|
" A positive numerical value that determines the termination condition of the\n",
|
|
" K-Means algorithm. You MUST call `k_means` with this threshold.\n",
|
|
" n_init: int\n",
|
|
" No. of times to run the K-Means algorithm before the best solution is\n",
|
|
" picked and used for compression.\n",
|
|
" random_state: int or `None`\n",
|
|
" Used to make the algorithm deterministic, if specified. You MUST call\n",
|
|
" `k_means` with `random_state` to ensure reproducility.\n",
|
|
"\n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" An `ndarray` with the shape `(h, w, 3)`, representing the compressed image\n",
|
|
" which only contains `n_colours` colours. Note that the entries should be \n",
|
|
" integers, not doubles or floats.\n",
|
|
" '''\n",
|
|
" # TODO: add your solution here and remove `raise NotImplementedError`\n",
|
|
" # no loop allowed\n",
|
|
" flattened_image = image.reshape(-1, 3)\n",
|
|
" labels, centroids = k_means(flattened_image, n_colours, threshold, n_init, random_state)\n",
|
|
" compressed_image = centroids[labels]\n",
|
|
" compressed_image = compressed_image.reshape(image.shape)\n",
|
|
" return compressed_image.astype(int)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:40.246659Z",
|
|
"start_time": "2024-04-13T11:07:39.525823Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"test_compressed_121 = compress_image(load_rgb_image(IMAGE_FILE_PATH), 5, 25.5, random_state=2109)\n",
|
|
"assert np.all(test_compressed_121.shape == load_rgb_image(IMAGE_FILE_PATH).shape)\n",
|
|
"\n",
|
|
"# Public test case 2\n",
|
|
"output_n_colours_121 = np.unique(test_compressed_121.reshape(-1, 3), axis=0).shape[0]\n",
|
|
"assert output_n_colours_121 == 5\n",
|
|
"\n",
|
|
"# Public test case 3\n",
|
|
"assert np.issubdtype(test_compressed_121.reshape(-1, 3).dtype, np.int_)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Displaying the Image After Compression\n",
|
|
"\n",
|
|
"Let us have a look at the compressed image!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 88,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:40.385219Z",
|
|
"start_time": "2024-04-13T11:07:40.248189Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"display_image(load_rgb_image(IMAGE_FILE_PATH))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"display_image(compress_image(load_rgb_image(IMAGE_FILE_PATH), 64, 25.5, random_state=2109))"
|
|
],
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:41.739175Z",
|
|
"start_time": "2024-04-13T11:07:40.385944Z"
|
|
}
|
|
},
|
|
"execution_count": 89
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Expected comparison between original and compressed image for your reference:\n",
|
|
"\n",
|
|
"<table>\n",
|
|
"<tr align=\"center\">\n",
|
|
" <td><img src=\"images/teddy_bear.jpg\" width=\"300\"></td>\n",
|
|
" <td><img src=\"images/compressed_image.png\" width=\"300\"></td>\n",
|
|
"</tr> \n",
|
|
"</table>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Part 2: Image Classification\n",
|
|
"\n",
|
|
"In this section, we shall help Gnoel with the problem of recognising handwritten\n",
|
|
"digits.\n",
|
|
"\n",
|
|
"**IMPORTANT**: Throughout this problem set, you may assume that each image is\n",
|
|
"monochrome (i.e. has only one channel, instead of 3 channels) and that each\n",
|
|
"image is 28*28 in size.\n",
|
|
"\n",
|
|
"**Gentle reminder that there is penalty for using iterative method where numpy is possible. We have written down the number of loops needed in each of the task as a comment.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Background\n",
|
|
"\n",
|
|
"A few months ago, Gnoel was appointed as the CEO of the postal service Iaiml, and weeks into the appointment, he realised how acute the manpower shortage is. To exacerbate matters, in recent years, Iaiml's profit margins have been narrowing, making the option of hiring more employees unviable. Observing this, Gnoel decides to automate certain processes.\n",
|
|
"\n",
|
|
"One such process is the entry of postal codes into the system. Specifically, instead of hiring people to look at each envelope and keying in the postal code manually, he wants to develop a machine that is capable of capturing these postal codes and recognising each digit of the postal codes, so that they can be entered into the system automatically. \n",
|
|
"\n",
|
|
"He has managed to get an engineer to develop a machine that can automatically capture images of individual digits in handwritten postal codes. However, the task of automatically recognising handwritten digits is still incomplete, and upon learning that you are about to complete a course in artificial intelligence and machine learning, he has approached you to work on the latter task: the recognition of handwritten digits."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## About the Data\n",
|
|
"\n",
|
|
"Leveraging on the aforementioned machine, Gnoel can create a dataset consisting of **many images of handwritten digits**. Unfortunately, due to the low profits Iaiml has been making, he has difficulty paying people to label them, resulting in a **much smaller dataset of labelled images**. The unlabelled data can be found in **digits_train.csv** and the lablled data can be found in **digits_validation.csv**(The data is in fact obtained from the MNIST dataset.). **You may assume that all images have dimensions $28 \\times 28$.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Load Digits Data\n",
|
|
"\n",
|
|
"The following lines of code load the required data. To be specific, `train_digits`\n",
|
|
"is a 2D matrix such that `train_digits[i].reshape((28, 28))` returns the monochrome image of the \n",
|
|
"`i`th handwritten digit that is found in the training dataset. The same can be\n",
|
|
"said about `validation_digits`, with the difference that the handwritten digits\n",
|
|
"are obtained from the validation dataset. Lastly, `validation_labels` returns\n",
|
|
"the label of each handwritten digit that is in the validation dataset. In other\n",
|
|
"words, `validation_labels[i]` returns the true label for `validation_digits[i]`.\n",
|
|
"For example, if `validation_digits[i]` is the image of the number 9,\n",
|
|
"`validation_labels[i]` will be equal to 9."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 147,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T12:33:23.836035Z",
|
|
"start_time": "2024-04-13T12:33:23.437488Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "((2900, 784), (100, 784), (100,))"
|
|
},
|
|
"execution_count": 147,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"train_data = load_digits_data_train()\n",
|
|
"validation_data = load_digits_data_validation()\n",
|
|
"\n",
|
|
"train_digits = train_data\n",
|
|
"validation_digits = validation_data[0]\n",
|
|
"validation_labels = validation_data[1]\n",
|
|
"train_digits.shape, validation_digits.shape, validation_labels.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Part 2.1: Classification Using K-Means Clustering\n",
|
|
"\n",
|
|
"In this subsection, we shall attempt to classify (and hence recognise) the\n",
|
|
"handwritten digits using the K-Means clustering algorithm which we have \n",
|
|
"implemented previously."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.1.1: Mapping Clusters to Digit Labels\n",
|
|
"\n",
|
|
"Now that we have the centroids, given any new sample $x_i$, we can easily predict which cluster it belongs to. However, this is not very helpful since Gnoel wants to know the **digits, not the cluster assignment**, of the postal codes. After all, there is not much that he could do, if he just knew that $x_i$ belongs to cluster 5, for example. Instead, he wants to know what digit $x_i$ represents. Therefore, we need to map each cluster to a digit, so that after finding that $x_i$ has a particular cluster assignment, we can tell Gnoel what digit it represents using this mapping. This is what we shall do in this question.\n",
|
|
"\n",
|
|
"In this task, **you are to find `cluster_to_digit`** (a 1D NumPy array) such that\n",
|
|
"`cluster_to_digit[i]` indicates which digit the `i`th\n",
|
|
"cluster represents, **when the clusters are obtained using the given values**\n",
|
|
"for `n_clusters=10`, `threshold=2`, `n_init=5` and `random_state=2109` with the\n",
|
|
"K-Means algorithm. For instance, if sample points that\n",
|
|
"are assigned to the 5th cluster are meant to be the digit 0, `cluster_to_digit[5]`\n",
|
|
"should return 0.\n",
|
|
"\n",
|
|
"On Coursemology, in addition to stating the value of `cluster_to_digit`, **please also describe\n",
|
|
"how you found it**.\n",
|
|
"\n",
|
|
"NOTE: you may find the helper function `display_image` helpful here."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 240,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:10:52.326192Z",
|
|
"start_time": "2024-04-13T14:10:44.687476Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(10, 784)\n",
|
|
"(2900,)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 10 Axes>",
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"labels, centroids = k_means(train_digits, 10, 2, n_init=5, random_state=2109) # NOTE: do NOT modify this\n",
|
|
"print(centroids.shape)\n",
|
|
"print(labels.shape)\n",
|
|
"# display_image(centroids[5].reshape(28, 28))\n",
|
|
"# display_image(train_digits[labels == 3][4].reshape(28, 28))\n",
|
|
"# plt.figure()\n",
|
|
"f,axes = plt.subplots(2,5)\n",
|
|
"axes[0][0].imshow(centroids[0].reshape(28, 28))\n",
|
|
"axes[0][1].imshow(centroids[1].reshape(28, 28))\n",
|
|
"axes[0][2].imshow(centroids[2].reshape(28, 28))\n",
|
|
"axes[0][3].imshow(centroids[3].reshape(28, 28))\n",
|
|
"axes[0][4].imshow(centroids[4].reshape(28, 28))\n",
|
|
"\n",
|
|
"axes[1][0].imshow(centroids[5].reshape(28, 28))\n",
|
|
"axes[1][1].imshow(centroids[6].reshape(28, 28))\n",
|
|
"axes[1][2].imshow(centroids[7].reshape(28, 28))\n",
|
|
"axes[1][3].imshow(centroids[8].reshape(28, 28))\n",
|
|
"axes[1][4].imshow(centroids[9].reshape(28, 28))\n",
|
|
"\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# TODO: you MAY add any code that you need to find `cluster_to_digit` here.\n",
|
|
"# However, you DO NOT have to submit this code snippet. Instead, explain how\n",
|
|
"# you found your solution in words on Coursemology. Feel free to add more cells\n",
|
|
"# below, if you need to."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 229,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:04:43.887374Z",
|
|
"start_time": "2024-04-13T14:04:43.877924Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"cluster_to_digit = np.array([8, 1, 6, 5, 3, 4, 7, 9, 0, 2]) # TODO: replace the '0's with the values that you have found"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.1.2: Predicting Labels with K-Means\n",
|
|
"\n",
|
|
"Your next task for part 2.1 is to implement `predict_labels_kmeans`.\n",
|
|
"\n",
|
|
"Now, let us implement the function `predict_labels_kmeans` (which we will later use in task 2.2.7) to predict the *digit labels* of each digit image. \n",
|
|
"\n",
|
|
"This function accepts `centroids`, the centroids of the clusters found using K-Means clustering, `cluster_to_digit`, which maps each cluster to the digit it represents, and `digits`, which represents the images of digits whose digit labels are to be determined.\n",
|
|
"\n",
|
|
"Then, it returns the predicted digit labels for each digit image in `digits`. Suppose the returned value is `pred_labels`, then `pred_labels` should be such that `pred_labels[i]` gives the predicted digit label for the $i$-th digit image in `digits`.\n",
|
|
"\n",
|
|
"You can use functions implemented earlier."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:07:42.455491Z",
|
|
"start_time": "2024-04-13T11:07:42.455371Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def predict_labels_kmeans(centroids, cluster_to_digit, digits):\n",
|
|
" '''\n",
|
|
" Predicts the digit labels for each digit in `digits`.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" centroids: np.darray\n",
|
|
" The centroids of the clusters. Specifically, `centroids[j]` should represent\n",
|
|
" the `j`th cluster's centroid.\n",
|
|
" cluster_to_digit: np.darray\n",
|
|
" A 1D array such that `cluster_to_digit[j]` indicates which digit the `j`th\n",
|
|
" cluster represents. For example, if the 5th cluster represents the digit 0,\n",
|
|
" then `cluster_to_digit[5]` should evaluate to 0.\n",
|
|
" digits: np.darray\n",
|
|
" An `m * n` matrix, where `m` is the number of handwritten digits and `n` is\n",
|
|
" equal to 28*28. In particular, `digits[i]` represents the image\n",
|
|
" of the `i`th handwritten digit that is in the data set.\n",
|
|
" \n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" A 1D np.darray `pred_labels` with `m` entries such that `pred_labels[i]`\n",
|
|
" returns the predicted digit label for the image that is represented by\n",
|
|
" `digits[i]`.\n",
|
|
" '''\n",
|
|
" # assign a cluster to each digit\n",
|
|
" result = assign_clusters(digits, centroids)\n",
|
|
" return cluster_to_digit[result]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"test_centroids_212 = np.array([[ 0, 1], [100,101], [200,201], [300,301], [400,401]])\n",
|
|
"test_centroids_to_digits_212 = np.array([2,0,4,3,1])\n",
|
|
"test_digits_212 = np.array([[6.36961687e-01,2.69786714e-01], [4.09735239e-02,1.65276355e-02], [8.13270239e-01,9.12755577e-01], [6.06635776e-01,7.29496561e-01], [1.00543625e+02,1.00935072e+02], [1.00815854e+02,1.00002739e+02], [1.00857404e+02,1.00033586e+02], [1.00729655e+02,1.00175656e+02], [2.00863179e+02,2.00541461e+02], [2.00299712e+02,2.00422687e+02], [2.00028320e+02,2.00124283e+02], [2.00670624e+02,2.00647190e+02], [3.00615385e+02,3.00383678e+02], [3.00997210e+02,3.00980835e+02], [3.00685542e+02,3.00650459e+02], [3.00688447e+02,3.00388921e+02], [4.00135097e+02,4.00721488e+02], [4.00525354e+02,4.00310242e+02], [4.00485835e+02,4.00889488e+02], [4.00934044e+02,4.00357795e+02]])\n",
|
|
"expected_digits_212 = np.array([2,2,2,2,0,0,0,0,4,4,4,4,3,3,3,3,1,1,1,1])\n",
|
|
"\n",
|
|
"assert np.all(predict_labels_kmeans(test_centroids_212, test_centroids_to_digits_212, test_digits_212) == expected_digits_212)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Part 2.2: Exploring PCA for Optimisation\n",
|
|
"\n",
|
|
"A couple of days later, your friend Ada hears about the project that you are working on, and has a cursory look at it. Looking at the data, she wonders whether your approach can be improved with the help of *dimensionality reduction* techniques. She urges you to try applying *principle component analysis* (PCA) on your data, **before** clustering is done. Intrigued, you decide to give it a shot.\n",
|
|
"\n",
|
|
"##### **IMPORTANT**: In the rest of this problem set, we shall follow scikit-learn's convention where `X` is defined to be an $m \\times n$ matrix such that `X[i]` returns the features for the $i$-th sample."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## (Informal Re-)Introduction to PCA\n",
|
|
"This subsection introduces the principles of PCA. If you are already familiar with PCA, you can skip this section and go to the next section.\n",
|
|
"\n",
|
|
"##### Understanding Data as Signal and Noise\n",
|
|
"Before we can get into PCA, we need a common understanding of what *data* really comprises of. In particular, one way of viewing data is that it is composed of *noise* and *signal*. Here, *signal* simply refers to the 'true' underlying measurements, in the absence of *noise*. Now, it should be clear that a dataset that is of 'high quality' is one that has high signal but low noise. \n",
|
|
"\n",
|
|
"##### Importance of Feature Variation\n",
|
|
"Assuming we have a high-quality dataset, we expect the features that are critical for differentiating one class of objects from another to be significantly different. Therefore, the variation in these features among the different data points should be higher. In contrast, features that are less critical will tend to have no, or minimal variation that is caused by (the low level of) noise. \n",
|
|
"\n",
|
|
"As a result, it makes sense for us to ignore features having less variation, but retain features having higher variation. Not only will this help us to get rid of features that are unimportant or noisy, it will also simplify the data by reducing its dimension, in turn making processing more efficient.\n",
|
|
"\n",
|
|
"##### Example: Dimension Reduction of Binary Images\n",
|
|
"To make things more concrete, let us consider a simple example. Suppose we have two binary images (i.e. images where each pixel is either 0 or 1, with 0 resulting in a black pixel and 1 resulting in a white pixel) as shown below. Moreover, to represent each binary we can represent each binary image as a 1D array of size 25, denoting each pixel.\n",
|
|
"\n",
|
|
"<table>\n",
|
|
"<tr align=\"center\">\n",
|
|
" <td><img src=\"images/zero.png\" width=\"300\"></td>\n",
|
|
" <td><img src=\"images/one.png\" width=\"300\"></td>\n",
|
|
"</tr>\n",
|
|
"</table>\n",
|
|
"\n",
|
|
"It should be obvious that, in fact, the 16 pixels around the perimeter will not help us to differentiate the image '0' from the image '1', since they have the same values (0) in both images. In other words, we can ignore these features, and instead represent each image with a 1D array of size 9, where it represents the $3 \\times 3$ in the center. Notice that this array has significantly fewer entries than the original one, i.e. the dimension of the data has been reduced, and we have effectively changed the coordinate system when changing the image's data representation. In general, this is what we hope to achieve: **reduce the dimension of the data while maintaining most of the variation in the data**.\n",
|
|
"\n",
|
|
"##### Dimension Reduction with PCA\n",
|
|
"Unfortunately, in most real world scenarios, features that are deemed less relevant are not so obvious. In fact, this is exacerbated by the presence of noise and more complex data. Thus, we need a more sophisticated and systematic way to do dimensionality reduction. \n",
|
|
"\n",
|
|
"Specifically, *variance* is used to quantify the variation in the features of different data points, and results from linear algebra (more precisely, eigenvectors and SVD) are used to determine how the coordinate system should be changed such that the variance of the data points, as defined in this new coordinate system, is maximized. In essence, this is what PCA does.\n",
|
|
"\n",
|
|
"For example, given data points with $D$ features, PCA will systematically transform these $D$ features into $N$ features (often, these new $N$ features are a combination of some features of the original dataset, before PCA is applied.), where $N \\leq D$ and $N$ is a user-specified value, such that the variation of the transformed data along these $N$ axes is maximised."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.2.1 Implementing PCA with SVD\n",
|
|
"Now that we have a high level idea understanding of how PCA works, why don't we have a closer look at it by trying to implement PCA with what we have seen during the lecture? \n",
|
|
"\n",
|
|
"Please implement `my_pca` which performs PCA as described in lecture 11. This function takes `X` and `n_components` as inputs. \n",
|
|
"\n",
|
|
"**IMPORTANT:** Here, **unlike what is shown in lecture**, `X[i]` represents the features of the $i$-th sample, and you **should center `X` before using it for any computations** (i.e. you should use `X`' in your computations, where `X`' is identical to `X` except that every feature of `X`' has a mean of 0). Besides these differences, your solution should be same as that shown in lectures.\n",
|
|
"\n",
|
|
"You should use [`np.linalg.svd`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html) to do SVD. \n",
|
|
"\n",
|
|
"This function should return a tuple `(components, singular_values)`, where `components` is an `n_components` $\\times$ `n` matrix such that `components[i]` returns the $i$-th principal axis (or component) that has the $i$-th largest singular value. In addition, `singular_values` is a 1D Numpy array with `n_components` entries such that `singular_values[i]` returns the $i$-th singular value.\n",
|
|
"\n",
|
|
"**Hint:** Because of how the rows of `X` in this problem set are the columns of `X` in the lecture slides, you **MUST** consider the transpose of the matrices, wherever relevant. All other aspects of the computations, besides the aforementioned ones, should remain the same."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 139,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:44:46.095046Z",
|
|
"start_time": "2024-04-13T11:44:46.086236Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def my_pca(X, n_components):\n",
|
|
" '''\n",
|
|
" Performs PCA on X to reduce it to `n_components`, using the method\n",
|
|
" described in lecture but with the 'centering' of X before SVD is done.\n",
|
|
"\n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" X: np.darray\n",
|
|
" An `m * n` matrix where `m` is the number of samples and `n` is the\n",
|
|
" number of features which each sample has. In other words, the `i`th sample\n",
|
|
" is given by `X[i]`.\n",
|
|
" n_components: int\n",
|
|
" No. of components that the reduced space has.\n",
|
|
" \n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" The tuple `(components, singular_values)`. Here, `components` is an\n",
|
|
" `n_components * n` matrix such that `components[i]` returns the `i`th\n",
|
|
" principal axis that has the `i`th largest singular value. In addition,\n",
|
|
" `singular_values` is a 1D numpy array with `n_components` entries such that\n",
|
|
" `singular_values[i]` returns the `i`th singular value.\n",
|
|
"\n",
|
|
" Note\n",
|
|
" ----\n",
|
|
" 'centering' here refers to subtracting the mean from X such that the resulting\n",
|
|
" X' has a mean of 0 for each feature.\n",
|
|
" '''\n",
|
|
" # TODO: add your solution here and remove `raise NotImplementedError`\n",
|
|
" # no loop allowed\n",
|
|
" # Center X\n",
|
|
" X = X - np.mean(X, axis=0)\n",
|
|
" cov = np.cov(X.T, bias=True)\n",
|
|
" # Not E, but S as singular, https://en.wikipedia.org/wiki/Singular_value_decomposition\n",
|
|
" U, S, V = np.linalg.svd(cov)\n",
|
|
" components = V[:n_components]\n",
|
|
" singular_values = S[:n_components]\n",
|
|
" return components, singular_values\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 140,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T11:48:38.658020Z",
|
|
"start_time": "2024-04-13T11:48:38.635191Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_test_1 = np.array([[ 0.16550775,-16.57982642,-50.13603715,-20.68348877, 8.80989065], [ 27.74489674, 17.10507978, 18.71626655, 32.02624683, 20.99010911], [ 18.05469443,-43.28464778, 31.50703811,-18.97769479, 0.44841138], [-45.96509892, 42.75939443, -0.08726752, 7.63493673,-30.24841115]])\n",
|
|
"expected_pca = my_pca(X_test_1, 2)\n",
|
|
"# Public test case 1\n",
|
|
"assert(not np.all(expected_pca[1] == np.array([7282.76644566, 4817.22025846])))\n",
|
|
"# Public test case 2\n",
|
|
"assert(not np.all(expected_pca[1] == np.array([2427.58881522, 1605.74008615])))\n",
|
|
"# Public test case 3\n",
|
|
"diff_singular_values = np.abs(expected_pca[1] - np.array([1820.69161142, 1204.30506462]))\n",
|
|
"assert(np.all(diff_singular_values < 0.000001))\n",
|
|
"\n",
|
|
"# Public test case 4\n",
|
|
"X_test_4 = np.array([[63.69616873,26.97867138, 4.09735239, 1.65276355,81.32702392], [91.27555773,60.66357758,72.9496561 ,54.36249915,93.50724238], [81.58535541, 0.27385002,85.74042766, 3.35855753,72.96554464], [17.56556206,86.31789223,54.14612202,29.97118905,42.26872212]])\n",
|
|
"diff_singular_values_4 = np.abs(my_pca(X_test_4, 2)[1] - np.array([1820.69161142,1204.30506462]))\n",
|
|
"assert(np.all(diff_singular_values_4 < 0.000001))\n",
|
|
"\n",
|
|
"# Public test case 5\n",
|
|
"X_test_5 = np.array([[0.22733602,0.31675834,0.79736546,0.67625467,0.39110955,0.33281393, 0.59830875], [0.18673419,0.67275604,0.94180287,0.24824571,0.94888115,0.66723745, 0.09589794], [0.44183967,0.88647992,0.6974535 ,0.32647286,0.73392816,0.22013496, 0.08159457], [0.1598956 ,0.34010018,0.46519315,0.26642103,0.8157764 ,0.19329439, 0.12946908]])\n",
|
|
"diff_singular_values_5 = np.abs(my_pca(X_test_5, 3)[1] - np.array([0.14205335,0.06453807,0.0472502]))\n",
|
|
"assert(np.all(diff_singular_values_5 < 0.000001))\n",
|
|
"\n",
|
|
"# Public test case 6\n",
|
|
"X_test_6 = np.array([[0.22733602,0.31675834,0.79736546,0.67625467,0.39110955,0.33281393, 0.59830875], [0.18673419,0.67275604,0.94180287,0.24824571,0.94888115,0.66723745, 0.09589794], [0.44183967,0.88647992,0.6974535 ,0.32647286,0.73392816,0.22013496, 0.08159457], [0.1598956 ,0.34010018,0.46519315,0.26642103,0.8157764 ,0.19329439, 0.12946908]])\n",
|
|
"diff_singular_values_6 = np.abs(my_pca(X_test_5, 3)[1] - np.array([0.14205335,0.06453807,0.0472502]))\n",
|
|
"assert(np.all(diff_singular_values_6 < 0.000001))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Performing PCA with Scikit-Learn\n",
|
|
"\n",
|
|
"Now that we know what PCA does in principle, let us have a look at how we can do it with the package Scikit-learn. The documentation for PCA can be found [here](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA).\n",
|
|
"\n",
|
|
"We have already imported PCA from Scikit-Learn in the beginning. The following sections will explain how we can use the package.\n",
|
|
"\n",
|
|
"Firstly, we need to create a PCA model.\n",
|
|
"```pca = PCA(n_components=70)```. In this case, we have chosen N to be 70. \n",
|
|
"Then, to find the 'right' coordinate system to which we want to transform the data, we need to do the following:\n",
|
|
"\n",
|
|
"`pca.fit(X) # where X is the dataset`\n",
|
|
"\n",
|
|
"Now, whenever we have some data `A`, where each column of `A` represents the same feature as that of `X`, we can transform it simply by making the following call:\n",
|
|
"\n",
|
|
"`A_transformed = pca.transform(A)`\n",
|
|
"\n",
|
|
"After being transformed, `A_transformed[i]` will have $N$ (transformed) features, where $N = 70$ because we have previously chosen $N$ to be 70. \n",
|
|
"\n",
|
|
"Recall that the transformation is done such that in the new coordinate system, most of the variation in `X` (before transformation) will be retained. Therefore, if each column of `A` and `X` represent the same feature(In fact, more importantly, we need `A` and `X` to be samples that are drawn from the same population.), we expect that most of the variation in `A` to be retained after transformation as well.\n",
|
|
"\n",
|
|
"Besides these, you might find `pca.explained_variance_ratio_` insightful. In particular, it returns an array of length $N$ such that the $i$-th entry indicates, out of the total variance in the data, how much of it is contributed by `X` along the $i$-th transformed axis. Note that the axes have been chosen such that an axis, along which there is more variation, is placed towards the left. Therefore, the values in `pca.explained_variance_ratio_` is non-increasing, similar to what we have seen in lecture 11. To put it differently, in the transformed coordinate system, axes that are more important come before those that are less important.\n",
|
|
"\n",
|
|
"Lastly, you may need `pca.inverse_transform(A_transformed)` for this problem set as well. This function works as follows. Suppose the output from this function is `A`'. Then, `A`' is an approximation of `A`(It will be exact, however, if we choose $N = D$, where $D$ is the number of axes in the original coordinate system). As such, if $N$ is chosen appropriately, we will expect `A`' and `A` to be close; however, if $N$ is chosen to be too small a value, `A`' might not represent `A` well since when transforming `A` to `A_transformed`, a significant amount of signal from `A` has been lost in the smaller coordinate system. \n",
|
|
"\n",
|
|
"For a concrete example, we recommend you to run the below code with different values of $N$."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 213,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T13:37:42.334968Z",
|
|
"start_time": "2024-04-13T13:37:42.176005Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(2900, 784)\n",
|
|
"(1, 70)\n",
|
|
"(1, 784)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"N = 70\n",
|
|
"# pca = PCA(n_components=N, random_state=2109)\n",
|
|
"# pca.fit(train_digits)\n",
|
|
"print(train_digits.shape)\n",
|
|
"transformed_digits = pca.transform(train_digits[1:2])\n",
|
|
"print(transformed_digits.shape)\n",
|
|
"# print(pca.explained_variance_ratio_)\n",
|
|
"approximated_digits = pca.inverse_transform(transformed_digits)\n",
|
|
"print(approximated_digits.shape)\n",
|
|
"display_image(approximated_digits[0].reshape((28, 28)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.2.2 (Bonus): Comparing `my_pca` with scikit-learn's `PCA`\n",
|
|
"\n",
|
|
"Run the following code. What is it about scikit-learn's implementation that\n",
|
|
"could have caused this discrepancy? **State the reason on Coursemology.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 181,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T12:54:43.038118Z",
|
|
"start_time": "2024-04-13T12:54:41.875607Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Singular values obtained with my_pca: [314493.08690992 237228.3638359 189209.97079049 165966.06636625\n",
|
|
" 157812.16391951]\n",
|
|
"Singular values obtained with scikit-learn's PCA: [30199.83364257 26229.03458239 23424.53660785 21938.58683831\n",
|
|
" 21392.87908082]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"_, singular_values_my_pca = my_pca(train_digits, 5)\n",
|
|
"sklearn_pca = PCA(n_components=5, svd_solver='full')\n",
|
|
"sklearn_pca.fit(train_digits)\n",
|
|
"singular_values_sklearn = sklearn_pca.singular_values_\n",
|
|
"\n",
|
|
"\n",
|
|
"print('Singular values obtained with my_pca: {}'.format(singular_values_my_pca))\n",
|
|
"print('Singular values obtained with scikit-learn\\'s PCA: {}'.format(singular_values_sklearn))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# TODO: you **may** want to write some code here to find the relationship between the two"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"You may want to type your solution here before copying it to Coursemology.\n",
|
|
"\n",
|
|
"[TODO, double click on the cell to access markdown]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.2.3: Finding Number of Components\n",
|
|
"Run the following code and find the least number of components needed to\n",
|
|
"obtain an explained variance of at least 99%. State your answer on Coursemology."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 186,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T12:56:40.600714Z",
|
|
"start_time": "2024-04-13T12:56:39.359528Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "TypeError",
|
|
"evalue": "'NoneType' object is not callable",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
|
|
"\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)",
|
|
"Cell \u001B[0;32mIn[186], line 3\u001B[0m\n\u001B[1;32m 1\u001B[0m full_pca \u001B[38;5;241m=\u001B[39m PCA(svd_solver\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfull\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m 2\u001B[0m full_pca\u001B[38;5;241m.\u001B[39mfit(train_digits)\n\u001B[0;32m----> 3\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[43mfull_pca\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mn_components\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m)\n",
|
|
"\u001B[0;31mTypeError\u001B[0m: 'NoneType' object is not callable"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"full_pca = PCA(svd_solver='full')\n",
|
|
"full_pca.fit(train_digits)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 193,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T13:09:49.641784Z",
|
|
"start_time": "2024-04-13T13:09:49.632936Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.9900574234907148\n",
|
|
"305\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# TODO: you **may** want to write some code here to find the answer\n",
|
|
"# Least number of components needed to obtain an explained variance of at least 99%\n",
|
|
"res = 0\n",
|
|
"variance = full_pca.explained_variance_ratio_\n",
|
|
"for i in range(len(variance)):\n",
|
|
" if np.sum(variance[0:i]) >= 0.99:\n",
|
|
" res = i\n",
|
|
" break\n",
|
|
"print(np.sum(variance[:res]))\n",
|
|
"print(res)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.2.4: Find K-Means Clusters with PCA\n",
|
|
"\n",
|
|
"Now that we are clear about how PCA works, let us try to perform K-Means clustering again. However, this time, we shall first reduce the dimensionality of our data using PCA, then do clustering. Please implement your solution in `find_kmeans_clusters_w_pca`. \n",
|
|
"\n",
|
|
"**IMPORTANT: From this task onwards, you should use scikit-learn's `PCA`**.\n",
|
|
"\n",
|
|
"The inputs which `find_kmeans_clusters_w_pca` takes are almost identical to those of `k_means`. The only difference is that `find_kmeans_clusters_w_pca` accepts an additional argument `n_components` which specifies $N$ for the PCA model.\n",
|
|
"\n",
|
|
"Here, the output should be `centroids` **and** `pca`, where `centroids` is an `n_categories` $\\times$ `n_components` matrix representing the centroids of the clusters in the transformed coordinate system (or space), and `pca` is the PCA model that is used to perform this transformation.\n",
|
|
"\n",
|
|
"**IMPORTANT**: please call `PCA` with `random_state` set to `find_kmeans_clusters_w_pca`'s `random_state` input value, and other than this argument and `n_components`, use the default values for the other arguments."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 233,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:07:55.981418Z",
|
|
"start_time": "2024-04-13T14:07:55.966498Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def find_kmeans_clusters_w_pca(digits, n_categories, threshold=2,\\\n",
|
|
" n_init=5, random_state=2109, n_components=70):\n",
|
|
" '''\n",
|
|
" Finds the centroids of the `n_categories` clusters given `digits` when PCA\n",
|
|
" is used to reduce the dimensionality of each image.\n",
|
|
" \n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" digits: np.darray\n",
|
|
" An `m * n` matrix, where `m` is the number of handwritten digits and `n` is\n",
|
|
" equal to 28*28. In particular, `digits[i]` represents the image of the `i`th\n",
|
|
" handwritten digit.\n",
|
|
" n_categories: int\n",
|
|
" The number of distinct digits.\n",
|
|
" threshold: double\n",
|
|
" Threshold that determines when the K-means algorithm should terminate. This\n",
|
|
" should be used with `k_means`.\n",
|
|
" n_init: int\n",
|
|
" The number of times to run the K-means algorithm before picking the best\n",
|
|
" cluster. This should be used with `k_means`.\n",
|
|
" random_state: int or `None`\n",
|
|
" Used to make the K-means and PCA deterministic, if specified.\n",
|
|
" n_components: int\n",
|
|
" The dimension to which each sample point is reduced, using PCA.\n",
|
|
"\n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" An `n_categories * n_components` matrix `centroids`, where `centroids[j]` is \n",
|
|
" the centroid of the `j`th cluster, AND the PCA model that is used to reduce\n",
|
|
" the dimension of each image.\n",
|
|
" '''\n",
|
|
" pca = PCA(n_components=n_components, random_state=random_state)\n",
|
|
" pca.fit(digits)\n",
|
|
" transformed_digits = pca.transform(digits)\n",
|
|
" _, centroids = k_means(transformed_digits, n_categories, threshold, n_init, random_state)\n",
|
|
" return centroids, pca"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 234,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:07:57.202199Z",
|
|
"start_time": "2024-04-13T14:07:57.154898Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# RUN THIS BEFORE RUNNING THE TEST CASES\n",
|
|
"expected_centroids_w_pca_224_1 = np.array([[ 4.41146747e+01,-3.97240692e+02, 5.10363670e+02,-2.94303812e+02, 1.90506902e+02, 3.19233952e+02,-4.04259213e+01, 4.93623367e+01, 1.17871222e+02,-3.86770830e+01,-9.76516902e+01,-1.10009417e+02, 8.75107032e+01, 3.95818261e+01,-1.76351356e+01,-6.05964921e+01, 1.03819916e+01,-1.25288033e+01,-2.43332562e+01,-1.65128997e+01, 5.17947320e+01, 2.04253628e+01,-3.64743512e+01,-1.08179498e-01, -3.62848940e+01, 3.95743508e+01,-2.76116198e+01, 1.25942325e+01, -3.09429039e+00,-6.46023523e+00,-1.42836685e+01,-1.59739468e+01, 1.96926009e+01, 4.48563092e+01,-2.02701257e+01, 4.91357703e+00, -5.62752563e+00,-1.19638851e+01,-2.68788441e-01, 2.49324434e+01, 1.25017490e+01, 8.93853870e+00,-8.25232052e+00, 3.31333914e-01, 6.97279189e+00,-6.29506935e+00, 3.42502194e+00, 2.76428108e+00, -9.26678271e+00,-1.26575625e+01, 3.24180853e+00,-1.17955053e+00, 1.40314375e-01,-1.56325905e-01, 1.86431042e+01, 9.49833455e+00, -4.37827561e+00, 9.69451007e+00,-2.45278573e+00,-1.40077762e+00, 3.46570617e-01,-4.85198866e-01, 1.25337573e+01, 1.93045344e+01, -5.07672424e+00, 1.28042647e+01,-1.05606510e+01, 7.13735274e+00, 3.85926281e+00, 2.66909049e+01], [ 3.43440646e+01,-1.23091183e+02, 1.07548194e+02,-7.52886984e+01, 7.26752358e+02,-1.82032031e+02, 4.36975586e+00,-2.03166773e+02, -1.83916710e+02, 1.74386500e+02,-4.72295635e+01, 3.51534865e+01, -1.47698829e+02,-1.75684242e+02, 9.42299448e+00,-8.00366704e+01, -4.56711498e+00, 1.30015040e+01,-9.56510388e+00,-1.84441031e+01, 2.73027865e+01, 5.13061191e+01, 1.71553294e+01, 1.10864802e+01, -2.58300107e+01,-1.47613401e+00, 5.62178397e+01,-1.15063340e+01, 4.22323402e+01, 1.86188465e+01, 1.84888723e+01,-1.20641515e+00, -6.70484759e+00, 2.54654573e+01,-2.39838440e+01,-6.00847360e+01, 2.96562151e+01, 2.60653689e+00, 1.31855395e+00,-4.31632588e+01, 1.39164063e+01,-3.44061968e+01,-6.51564742e+00, 1.19979383e+01, 8.48439346e+00, 1.77203872e+00,-1.68226999e+01, 1.22910593e+01, -8.96544753e+00,-1.12132900e+01,-1.43635179e+00, 1.92704604e+01, 1.56780309e+01,-7.01840642e+00, 1.44422724e+01,-6.72455336e+00, -6.70754344e+00, 3.63491824e+00,-1.16563011e+01,-1.33303015e+01, 1.55998897e+01,-5.20495273e-01, 2.92315368e+00, 2.61663260e+01, -1.47052507e+00,-1.52076924e+01,-7.34671156e+00, 9.36539774e-01, -3.98343537e+00,-2.31594779e+01], \n",
|
|
"[ 5.42512573e+02, 3.72441475e+02,-5.42300990e+02,-4.37470793e+02, -2.72885981e+02, 2.29801621e+02, 1.86715048e+02,-3.35192475e+01, -1.53562958e+02,-8.24332212e+01,-1.46676354e+02, 1.37485622e+02, 1.00007452e+02,-1.03944628e+02, 1.81981294e+01,-7.55301266e+01, -4.56164408e+01,-1.13126594e+02, 1.58593583e+02, 3.62892761e+00, 6.46159255e+01, 1.60160772e+01,-6.10785110e+01,-3.68514908e+01, -4.09507334e+01,-7.13715084e+01, 2.49666025e+01, 6.22057886e+01, 8.40325155e+01,-9.11768731e+00, 5.41995913e+00,-2.54173413e+00, -5.48196676e+01,-1.14936162e+01, 8.77622332e+00, 5.99599689e+00, 4.24541079e+01, 1.65025881e+01,-6.03472660e+01,-1.91215237e+01, 6.29597657e+00, 1.83801823e+01, 6.24994505e+00,-4.53187131e+01, -7.25410775e-01,-1.02947851e+01,-1.52242904e+01, 4.39179900e+00, 2.42872818e+01,-8.44650772e+00,-6.79063161e+00,-6.53665958e+00, 2.97647613e+00, 1.00251441e+01, 8.24597697e+00, 1.34548288e+01, 1.88195934e+01, 5.37531602e+00,-2.53018410e+00,-3.60797348e+00, 1.68866494e+01, 2.77009222e+00,-4.05828060e+00,-2.40365094e+01, 3.87731066e+00, 1.28769951e+01, 9.61629098e+00,-4.82477813e+00, -2.31731920e+01, 2.96323009e+01], [ 4.18430104e+02,-1.51347320e+02,-3.96564119e+02, 4.95129949e+02, 3.57119464e+01, 2.63481338e+02, 8.45772420e+01,-1.77284086e+02, 1.76166438e+02, 3.58547555e+01, 1.60395162e+02,-1.47683964e+02, -4.53100507e+01, 1.80592390e+01, 6.36212214e+00, 1.55955789e+01, -6.75646020e-01, 2.26235729e+01, 2.61805223e+01, 5.60129817e+01, 3.41668886e+01, 8.61248484e+00, 5.64006070e+01, 5.66359634e-02, 6.93045971e+01,-8.83478890e-01, 2.41318451e+01,-2.05315258e+01, -8.10876502e+00,-1.90097200e+01,-1.04925202e+01, 2.53856513e+01, 3.21287592e+01, 1.71577899e+01, 5.26021624e+00, 2.50365208e+01, -3.34141175e+01,-2.30566909e+01, 2.21844695e+01, 5.55631439e+01, 7.32181865e+00,-9.12010599e+00,-4.63559342e+00, 5.65810950e+00, 1.29628560e+01,-1.30115996e+01,-1.62649620e+01, 1.66616602e+01, -1.86278670e+01, 2.90698932e+00,-2.15473198e+01, 5.27303030e-01, 2.29989100e+01, 8.13134030e+00,-1.76255044e+01, 9.90687911e-01, 1.05291344e+01, 3.35019674e+00, 4.95391366e-01,-6.71659182e+00, 3.24891021e-01, 7.19777825e+00, 6.36114037e+00,-4.71738998e+00, -1.39844441e+01, 7.48267642e+00, 4.63335862e-01,-9.34191816e-03, 4.95674704e+00, 5.30044998e+00], \n",
|
|
"[ 1.12060551e+03,-1.36455661e+02, 4.44410760e+02,-1.70241314e+02, -3.75131886e+02,-2.44150073e+02, 1.76851461e+02,-8.27106514e+01, -1.54885659e+02,-7.17487515e+01, 8.59043925e+01, 8.73480630e+01, -5.24321813e+01, 1.00821971e+02, 2.07178482e+01, 4.37883293e+01, -1.12555778e+01, 5.88723098e+01,-8.62615752e+01,-8.37865871e+01, 1.79239348e+00, 3.54663483e+01, 3.05466808e+01, 3.57940326e+01, -4.89710854e+01, 6.58341271e+00,-4.86565692e-01,-3.24191990e+00, -3.44458898e+01, 6.61211842e+01,-4.69206169e+01,-2.63336958e+00, -2.13619140e+01,-4.99876853e+00, 1.08550700e+01, 1.03023122e+01, -1.76050641e+01,-2.11583816e+00, 3.75151251e+00,-1.66131028e+01, -2.44756684e+01,-3.75950280e+01, 1.13151909e+01, 2.10212080e+01, -1.63486464e+00,-2.93302748e+01, 1.21143034e+01,-2.41916273e+01, -1.42779557e+01, 2.39087355e+01, 4.35593256e+00,-3.79762649e+00, 1.26145973e+01,-2.82011849e+00, 1.76942385e+00,-9.77977014e+00, -9.62960284e+00,-1.31339799e+01, 1.24216326e+01, 1.04378407e+01, -1.38599878e+01, 2.49941068e+00,-7.90344061e+00, 1.73379627e+00, 2.17118294e+00,-4.18417877e+00, 1.86069586e+00,-4.24971310e+00, 2.19383586e+00,-1.72411423e+01], [-5.52535379e+01, 7.71965382e+02, 2.74104510e+01,-5.08908195e+00, 4.45893126e+01, 1.23319481e+02,-1.93089947e+02, 1.90492740e+02, 5.90320783e+01, 7.25606552e+01, 1.77159809e+01, 5.34649940e+01, -6.11711367e+01,-1.24044604e+01,-1.17744149e+01, 4.80718098e+01, -2.16122349e+01, 4.38216453e+01,-2.52624424e+01,-3.09068820e+01, -4.38387586e+01,-4.33357271e+01, 4.36241106e+01, 1.53499949e+01, -6.20469389e+00,-1.73483781e+00,-1.30571320e+01,-2.05304354e+01, -1.42329848e+01, 1.84480050e+01,-7.47640730e+00,-5.37361629e+00, 8.68450171e+00,-1.46462878e+01,-9.08625575e+00, 4.06766131e+00, -9.16679885e+00,-2.23068975e+00, 2.23343345e+00,-1.91224106e+01, -9.99488113e-01, 2.63105032e-01, 1.25432469e+01,-1.71861872e+00, -4.42326801e+00, 7.76926701e+00, 5.09126773e+00,-7.87003989e+00, 1.55707253e+00, 7.78635459e+00, 1.03502798e+01,-1.26310532e+00, -1.07387279e+00, 3.71581261e+00, 1.73787850e+00,-7.29575536e+00, 1.77893598e+00,-3.35477780e+00, 3.49765786e+00,-5.15289373e+00, -1.57630012e+01,-3.57840162e+00, 3.37421856e+00, 9.83777675e+00, -2.64231635e-01,-5.00400438e+00,-2.13908607e+00,-7.60240826e+00, 1.54003577e+00, 3.51617436e+00], [-5.45162066e+02, 4.03270879e+02, 6.06715970e-01, 1.51002925e+01, -5.07101624e+00,-3.14931468e+02, 1.34523793e+02,-1.77249314e+02, 8.96942889e+01,-2.42290292e+02, 1.33820981e+01,-1.08498357e+02, 1.00201308e+02, 1.08238002e+02,-5.69276090e+01,-1.29971347e+01, 6.71165749e+01,-4.67314218e+01,-3.88712306e+01, 4.60451475e+01, 6.77422676e+01, 7.61568044e+01,-3.36045770e+01, 2.28799130e+01, 4.32710880e+00,-2.94145237e+01,-3.80186969e+01, 1.38689582e+01, 4.22122478e+01, 1.82129713e+01,-2.35299154e+00, 1.14465743e+00, 1.55265606e+01, 3.22832540e+01, 2.56550533e+01,-8.69683472e+00, 9.93581807e+00, 2.63967435e+01, 1.46618134e+01, 1.29237755e+01, -1.20810232e+01, 1.23572494e+01,-8.54180532e+00, 1.76449060e+01, -6.36135619e+00, 2.92914076e+00, 4.34712848e+00,-2.06274381e+01, -2.33058512e+00, 1.41976981e+01,-1.40930133e+01,-4.14580970e+00, -8.23741132e+00,-2.71413534e+01,-1.13063841e+01, 6.71210824e+00, -3.44426192e+00,-1.07483990e+01, 4.60755911e+00, 1.03133662e+01, 2.21741658e+01, 5.81958885e+00, 6.36446606e+00,-9.58019774e+00, -6.67146225e+00,-7.92120607e+00,-4.52565882e-01, 2.48692454e+00, 7.00065888e+00,-5.47138954e+00], \n",
|
|
"[-6.45364553e+02,-3.38497486e+02,-1.19768064e+02,-2.76081711e+01, -2.72834450e+02, 7.16131031e+00, 1.31400901e+02, 4.02015371e+01, -1.92550905e+02, 1.19835617e+02, 2.51517205e+01, 7.45402302e+00, -4.35252866e+01,-4.99053071e+01,-5.10791627e+01,-3.18619761e+01, -5.36879445e+01, 4.91162949e+01, 4.23916491e-01,-8.74257624e+00, -2.04169627e+01,-2.61624382e+01, 1.96496614e+01, 6.04963167e+00, -5.35390017e+00, 9.49318251e+00, 1.08670768e+01, 4.87414136e+00, -4.89402461e+01,-1.46108382e+01,-8.67694875e+00, 1.97538878e+01, -2.26404177e+01, 4.09794519e+00,-1.30764156e+01, 1.24382142e+01, 7.98561827e+00, 3.46465074e+00, 1.27997552e+01,-4.87496162e+00, 6.41016347e+00,-1.04062743e+01, 1.92956145e+00,-8.23571428e+00, -6.11806436e-02, 3.47119078e+00, 4.27388240e+00, 8.76670160e+00, 1.18360498e+01,-1.37051722e+01, 1.33112873e+01,-1.92944334e+01, 3.17165899e-01, 5.21790084e+00,-1.64927989e+00,-2.17068561e+00, 5.90546312e+00, 1.06862233e+01, 1.36063023e+01, 1.06981752e+01, -5.79931097e+00,-1.40877429e+00, 2.50774011e+00,-1.49355688e-02, 4.44072835e+00,-1.04471397e+01,-2.38780677e-01,-3.44774438e+00, 2.98751327e+00,-2.66429625e+00], [ 1.06422031e+02,-5.64896355e+02,-5.12447063e+02,-3.13063449e+02, 1.75046324e+02,-3.16619684e+02,-3.58609170e+02, 1.01262787e+02, 1.38283966e+02,-3.39390631e+01, 1.19905806e+01, 2.38305815e+01, -6.18608786e+01, 4.48008535e+01, 7.46826115e+01, 8.40711068e+01, 3.05078110e+01,-3.77220193e+01,-5.15560875e+01, 5.44343873e+00, -1.04892299e+02,-4.75447320e+01,-2.94984003e+01,-4.05145593e+01, 4.20428521e+01, 2.33063286e+01,-5.31443550e+01, 2.78089346e+01, -2.69984185e+01,-3.58682522e+01, 7.10119883e+01,-1.11766248e+00, 1.56755099e+01,-4.70852490e+01, 4.75171397e+00,-1.98030687e+01, -2.59404353e+01,-6.12271258e+00,-4.89949549e+00, 6.54106154e+00, -8.99944501e+00, 1.61529354e+01,-2.47137300e+01, 2.89591576e+01, -1.74563972e+01, 1.38589359e+01, 7.44935550e+00, 1.08601723e+01, 3.49782615e+01,-5.69632133e+00,-5.15360633e+00, 4.69749172e+00, -4.46562617e+01, 1.31605354e+01, 4.85031956e-01,-7.12623655e+00, 4.31888389e+00,-1.75006696e+01,-2.47676077e+00, 3.87669529e+00, -1.12151449e+01, 6.62372114e+00,-2.06358428e+01,-1.99299771e+01, 1.71748148e+01, 3.62355853e+00,-2.79567223e-01, 6.45987528e+00, -2.99107924e+00,-1.19641639e+01], [ 1.19074635e+01,-2.43454003e+02, 3.55970817e+02, 5.75802412e+02, -7.44983668e+01,-6.89205998e+01,-4.35919215e+01, 9.28077942e+01, 1.87988860e+00,-3.28456999e+00,-9.60548121e+01, 1.16867124e+02, 1.31849423e+02,-2.06119510e+01, 9.19474493e+01, 3.86233578e+01, 4.77644846e+01,-5.83683976e+01, 1.04625836e+02, 3.99118964e+01, -4.45226445e+01,-4.09858315e+01,-5.64607207e+01,-4.18741894e+01, 2.79648900e+01, 1.12700271e+00, 4.83678585e+01,-3.52296649e+01, 3.09760086e+01,-2.95721182e+01, 1.76872410e+01,-2.79859432e+01, -1.17860436e+01,-5.87864073e+01, 2.25648750e+01,-3.07943628e+00, 2.06323140e+01,-1.17400689e+00,-2.73069594e+01,-1.43592350e+01, -4.02953240e+00, 2.76952318e+01, 1.28357472e+01,-3.11077566e+01, 4.11173180e+00, 1.45354567e+01,-1.65581596e+00, 1.36317524e+00, -1.66775559e+01, 2.98608229e+00, 3.34336530e+00, 2.98150001e+01, 1.82567576e+00,-8.55034971e-01,-6.23455461e+00, 5.27499314e+00, -1.88687544e+01, 5.34495534e+00,-2.95999039e+01,-1.20385632e+01, 7.29039546e+00,-1.47986691e+01,-1.54767095e+01,-9.91021339e+00, 4.83639937e+00, 1.50155518e+01, 1.42761462e+01, 7.88473662e+00, -7.48990170e+00,-1.20567202e+01]])\n",
|
|
"\n",
|
|
"expected_centroids_w_pca_224_2 = np.array([[-1.26388565e+02,-2.97805287e+02,-2.58743997e+02, 1.00615071e+02, 5.02583335e+02, 3.28468793e+02,-1.97043507e+02,-1.52104865e+02, -5.64894637e+01,-6.50940115e+01,-7.84923220e+01, 8.08940693e+01, 8.30301290e+01,-6.10133258e+00, 5.62609498e+00, 2.98112921e+01, 5.79705249e+01,-3.23610418e+01,-6.18559790e+01, 3.03730839e-01, -1.86899661e+01, 1.82035617e+01, 3.12677467e+01, 3.56583600e+01, 4.19771050e+01,-1.85904780e+01,-2.53463565e+01,-1.05794033e+01, -1.33036091e+01, 2.01308022e+01,-3.78505397e+01,-1.77072969e+01, -4.71928792e+01, 6.48880912e+00,-1.46476746e+01, 2.94119449e+01, -2.25368783e+01, 3.45956284e+01,-1.77294999e+01, 1.10087990e+01, 7.65872139e+00, 2.75045313e-01,-2.71896086e+00, 3.29257643e+01, -3.89132685e+00,-6.77181749e+00, 1.49421267e+00, 1.91614813e+01, -3.21099709e+00,-1.90989527e+01,-2.09268969e+00, 2.05412972e-01, -1.08773947e+01, 4.23956700e+00, 5.86144814e+00,-1.26752766e+01, -9.02383537e+00, 2.97634207e+00,-1.57723947e+01,-1.52569799e+01, -9.56215421e+00, 3.14401800e+00,-5.98361137e-01,-1.31087420e+01, -3.89557120e+00,-2.65180031e+00,-5.95495391e-01,-1.70958031e+01, 2.51495818e+00,-7.94813542e-01], [-4.27127711e+02, 1.02112004e+02,-1.55652325e+02, 1.93190981e+02, -2.83949541e+02, 3.44295520e+02, 8.51281189e+01,-1.80725999e+02, 4.47518188e+01, 2.48792779e+02, 1.43692247e+02, 2.96391432e+01, -3.37104349e+01, 1.09416598e+02,-5.94294589e+01,-4.18754981e+01, -2.35625110e+01, 5.65065601e+01, 2.04049206e+01, 3.16945974e+01, -5.71732561e+01, 6.26170201e+00,-1.30573458e+01, 2.68043449e+00, 6.40841440e-01,-6.89966768e-01, 7.18935871e+01,-2.82163385e+01, -6.35679482e+00,-1.33744524e+01,-2.87496949e+01, 2.07935311e+00, 5.34529825e+01,-2.56800761e+01, 2.92083638e+01, 8.81333815e+00, 5.87736029e-01,-1.90430028e+01, 7.97475361e+00,-8.01118257e+00, 1.97691695e+01, 6.07898032e+00, 1.52161491e+01,-2.46436129e+01, 6.00204131e+00,-1.29307947e+00,-3.01700519e+00, 1.33351673e+01, 1.27192394e+00,-2.01935980e+00,-2.22903861e+00,-1.12494427e+01, 5.20021300e+00,-1.76564360e+01, 1.51603827e+00,-4.68291645e-01, 8.96443912e+00,-2.41755371e+00, 1.49192165e+01,-8.97123612e+00, -2.16930602e-01, 4.64353232e+00,-2.98663400e+00, 9.49778486e+00, -1.89359394e+01, 8.66311571e+00, 1.67220618e-01, 1.36275486e+01, -5.48040192e+00,-6.33633372e+00], \n",
|
|
"[ 7.30342241e+00,-3.65815706e+01, 7.71343648e+02, 1.19995251e+02, 2.52115792e+02, 2.09521800e+02,-5.41087703e+01, 4.25014533e+01, 1.20264938e+01,-1.49105376e+01,-9.54135315e+01,-2.02442276e+02, -5.75183700e+01, 4.96059254e+01, 5.29069159e+01, 1.08046060e+01, -7.43889273e+01, 5.46290908e+01, 9.15506130e+01,-2.41728913e+01, 3.77485306e+01,-6.47768124e+01, 3.43599647e+01,-1.75240369e+01, 4.11793238e+01,-2.58860442e+01, 2.50056855e+01, 6.06019998e+01, 5.46670704e+00,-5.60167792e+00, 1.49167495e+01,-5.58110256e+01, -1.76963563e-01, 5.70668693e+00, 4.82397188e+00,-1.84367370e+01, -2.40983652e-01, 1.44345176e+01,-5.06131757e+00,-3.11427765e+01, -9.92773176e+00, 2.86438312e+01,-1.44776983e+00, 1.84709312e+01, -1.26449944e+01, 1.39275803e+01,-3.41715401e+01,-7.27993319e+00, 1.72786868e+00, 5.15367110e+00, 1.95172609e+01,-1.32312629e+00, -1.47806734e+01, 1.28764763e+01,-1.50327360e+01, 1.06917986e+01, 7.10508768e-02, 1.32964932e+00, 6.76354662e+00, 1.75769812e+01, 3.16377725e+00, 1.29947054e+01,-3.00768287e+00, 2.38532344e-01, 1.46436202e+01,-2.67031436e-01, 3.92186734e+00,-9.61135382e+00, -2.07674276e+00, 2.15972506e+01], [ 8.87055326e+02,-4.73953190e+02,-2.54585667e+02, 6.01935830e+01, -3.48562790e+02, 4.16890508e+01, 4.92896433e+01, 2.29121937e+01, -5.18938683e+01,-1.03170742e+01,-1.16811216e+02, 1.50231640e+00, 2.93788756e+01,-5.30804431e+01,-6.19988477e+01, 6.69682724e+01, -4.56218002e+01, 1.56257007e+01, 5.90474183e+01,-1.47563014e+01, 4.77898559e+01,-3.88452335e+00,-2.33285246e+01,-2.25245752e+01, 2.03699393e+01, 2.85623812e+01,-3.15848415e+01,-2.51899693e-01, -3.59985424e+00,-2.48509077e+01,-4.12058553e+00, 1.67990293e+01, 3.82887966e+01, 2.56993471e+00,-1.10366191e+00,-4.63537857e+00, 5.49538107e+00, 1.08566108e+01, 1.67585158e+01, 3.40525903e+00, 7.74803196e+00, 6.80137534e+00, 1.63525233e+00,-6.00814154e+00, 1.15567997e+01,-2.44926871e+01,-1.23929628e+01, 2.63593247e+00, 5.11825597e+00, 7.56695233e+00,-3.23694567e+00,-1.12203495e+01, -1.32480515e+01,-1.00578885e+01,-7.42366384e+00, 1.49354274e+00, 1.28891916e+00, 3.38331782e+00,-6.31592338e+00,-2.10950090e+00, 8.96593127e+00,-1.64190946e+00,-6.15841147e+00, 5.59989196e-01, 4.02321104e+00,-3.84044857e-01, 1.80999072e-01,-1.37985385e+00, -3.59647873e+00,-4.73164060e+00], \n",
|
|
"[ 1.14878099e+02,-4.07198554e+02, 4.61796715e+02,-2.97223374e+02, -7.56619365e+00,-2.73304315e+02,-3.90025713e+01,-1.30803032e+02, -5.80394935e+01, 6.81767516e+01, 1.65526170e+02, 6.41234339e+01, -1.22749575e+01, 1.98967425e+01, 1.26353175e+01,-6.56754638e+01, 5.57208275e+01,-5.90685517e+01,-1.92238999e+01, 3.22419918e+01, -1.17113813e+01,-1.69949990e+01,-8.38756648e+00,-2.23753469e+00, -5.01484485e+01,-1.44251213e+01,-1.09130684e+01,-6.17259649e+00, 9.07230007e+00, 9.72050792e+00, 1.50627573e+01, 2.07155795e+01, -1.05532046e+01,-3.74508975e+00, 7.86780764e+00, 8.30350039e+00, -2.00550343e+01,-2.21286476e+01, 2.34028712e+00, 1.10469562e+01, -1.47375107e+01,-1.50497263e+01,-3.60418058e+00,-2.42002993e+00, -7.12710353e+00, 2.34342561e+01, 2.19286197e+01,-3.97570225e+00, -1.18353991e+01, 2.06350734e+01,-1.51887607e+01, 1.90050383e+01, 2.07672477e+01, 1.40153388e+01, 1.18544203e+01,-4.86768979e+00, 3.59160338e+00,-3.39675716e-01, 1.16604565e+01, 5.95547853e+00, -8.77526236e+00,-5.77276955e+00, 8.76950061e-01,-4.30492882e+00, -6.73568495e+00, 2.78415612e+00,-3.67409060e+00, 4.43330844e+00, 2.72411445e+00,-1.23609285e+01], [ 5.07964327e+01, 8.03847983e+02, 1.47970519e+02,-1.47574678e+02, -1.01787935e+02,-1.79848745e+02, 3.92763180e+01,-1.35019174e+02, -1.10137429e+02,-5.40912837e+01,-2.44985268e+02, 7.96534556e+00, 3.73218410e+01,-3.43040759e+01, 4.29686859e+01,-6.21636402e+01, 4.54911984e+01,-9.66315586e+01,-1.33631653e+01,-3.09297902e+00, 2.87907991e+01, 1.02710992e+02,-3.87210092e+00, 2.77750136e+00, -1.47482865e+01, 2.80063172e+01, 1.99914501e+01,-2.19591997e+01, 1.61525006e+01,-4.65915129e+00,-7.63458394e+00, 1.19997622e+01, -2.12722032e+00, 1.31113471e+01,-4.19335372e+00, 9.73158790e+00, 1.50172011e+01, 9.66324044e+00,-5.39076801e-01, 1.69845573e+01, 2.11338949e+01,-4.70998720e+01, 4.15485530e+01,-1.88128967e+00, 2.23149372e+01,-4.39298919e+00,-3.81536212e+00,-9.50214895e-01, 1.47295947e+01,-2.33507586e+01, 2.61344567e-01,-5.56920015e-01, -8.28569886e+00,-1.67097226e+01, 2.35812539e-01, 1.50111136e+01, -1.28047019e+01,-1.04092896e+01,-1.17495432e+01, 1.45667479e+00, -1.02381521e+01,-9.40006987e+00, 8.60128653e+00,-1.60734852e+01, -8.42021639e+00,-7.12568476e+00, 3.80456970e-02, 2.19313844e+01, 1.01802420e+01, 1.44614274e+01], \n",
|
|
"[-9.12756417e+02,-3.38137127e+02,-2.63820701e+02,-1.99539738e+02, -1.26082528e+02,-9.58456251e+01, 1.04748777e+02, 2.34488061e+02, 1.00198571e+02,-1.48576951e+02, 7.71709596e+00,-1.00658790e+02, -1.14270504e+02,-2.10290881e+02, 1.13920251e+01,-1.27935667e+00, 5.46372412e+00,-2.23327380e+01,-3.18059737e+01,-5.25426904e+01, 5.97048978e+01, 3.32956678e+01, 1.24808594e+01, 2.41200990e+00, 1.75113022e+01,-1.17454904e+01,-2.21165559e+00, 1.05380128e+01, -7.61339728e+00,-1.45626727e+01, 6.09720217e+00, 1.44986363e+01, 8.68079620e+00, 2.03277483e+01,-1.14992509e+01,-1.48743880e+01, 2.84458331e+01,-8.93107968e+00, 2.32848409e+00,-5.06794121e+00, -1.69981949e+01, 7.61324443e-01, 2.14601174e+01, 8.34595760e+00, 1.44880246e+01, 1.90409956e+01, 1.66206994e+01,-1.22043369e+01, 5.32662421e+00, 8.26407492e+00,-1.18310359e+01, 5.23545466e+00, -5.45694030e+00,-2.49864069e+00,-1.00819530e+01, 2.01403069e+01, -3.83977693e-01,-8.05006718e+00,-8.64281333e+00, 4.01073197e+00, 8.09685360e+00,-6.29900549e+00, 2.22854838e+00, 1.23562404e+00, 9.57440969e+00,-3.58419535e+00,-4.77853700e+00,-2.27794183e+00, -1.95346554e+00, 9.99333590e+00], [ 7.03510143e+02, 7.72662614e+02,-3.89594579e+02,-4.53536233e+02, 2.08846424e+02,-4.97963482e+01,-7.73959006e+01,-1.15561097e+02, 6.22516003e+02,-1.24013514e+02, 9.07694733e+01,-1.07516806e+02, -2.36572286e+02,-4.31079918e+01,-3.22350740e+01, 9.58878292e+01, -3.61595480e+01, 4.07744309e+01,-1.18791559e+02, 2.19283794e+01, -3.82647082e+01,-1.30730449e+02, 2.20748172e+01,-3.38565334e+01, 7.04624366e+01,-5.64984400e+01, 1.51425320e+00, 4.13387651e+01, -1.58021017e+01, 5.86248488e+01, 9.89627525e+01, 1.45006462e+01, 1.38748448e+01,-5.96952604e+00, 3.98478125e+01,-1.46488125e+00, 4.31329750e+01,-3.89831506e+01, 6.07284219e+00, 3.25244424e+01, -1.93655796e+01,-1.24887231e+01,-1.82207944e+01, 1.29325023e+01, -2.89361514e+01,-1.08617999e+01, 1.90830316e+00,-2.44475468e+01, 4.50141905e+00,-2.47165629e+01, 2.10874859e+01, 7.12675060e+00, 1.13722287e+01, 2.23350304e+01,-3.58505423e+00,-1.51730814e+01, 1.35161176e+01, 1.46591606e+01, 5.55032170e+00,-3.64542126e+01, -9.67565801e+00,-9.63481331e+00, 1.41461844e+01, 1.24280973e+01, 1.56694555e+01, 2.14233407e+01, 7.36861526e+00,-2.23506974e+01, -7.35275639e+00,-4.63868422e+01], \n",
|
|
"[ 1.32846098e+02, 9.69621217e+01,-3.18228377e+02, 2.99759315e+02, 3.93448184e+02,-4.41623203e+02, 1.00368149e+02, 2.68708729e+02, 2.96539923e+01, 8.34869121e+01, 8.30142591e+01,-3.85777872e+01, 6.93535937e+01, 1.96866655e+02, 2.20802476e+01,-1.00036487e-01, -3.54604091e+00, 2.21718010e+01, 3.47748735e+01, 2.31512372e+01, -7.21680826e+01, 7.93894071e+00,-3.02629921e-01, 1.26003931e+01, -4.40149916e+01, 4.79578317e+00, 7.30772816e+01,-2.46127052e+01, 1.33117396e+01,-5.63192088e+00,-2.55903851e+01, 2.12103526e+01, -2.97123604e+01,-2.13766602e+01,-3.81268310e+01,-7.01381488e+00, -2.02019953e+01, 4.95997852e+00, 1.15703840e+01,-1.46283414e+01, -8.84286238e+00, 3.04229202e+01,-4.04554495e+01,-2.11444014e+01, 9.73959925e+00, 1.23885002e+01, 2.07772896e+00, 8.74480664e+00, -1.03123477e+01,-4.84150473e+00,-1.24759366e+00,-1.18561892e+01, -4.18462016e-01, 2.03353723e+01, 4.56301562e+00,-1.09382975e+01, -4.97389816e+00, 9.21820003e+00, 1.03152241e+01, 2.18659688e+01, -5.87532560e+00, 7.76375291e+00,-1.14463218e+01, 4.56435968e+00, 6.12249448e+00,-1.19084995e+01, 1.03511151e+01,-1.53862305e+00, 4.25170796e-01, 1.36453629e+01], [-2.23984521e+02, 5.91900219e+02, 6.66123375e+01, 2.11365506e+02, -2.28553857e+02, 1.15539568e+02,-6.82331252e+01, 1.95554426e+02, -1.60216857e+02,-8.54581346e+01, 1.00543285e+02, 1.85450887e+02, 1.16641663e+02,-1.34183777e+01, 1.35907082e+01, 3.67474685e+01, -2.34828185e+01, 8.42292047e+01,-2.65620566e+01,-8.13582248e+00, -1.37414365e+01,-4.50434991e+01,-3.25063596e+01, 4.61653347e+00, -4.36265632e+01, 3.38759016e+01,-1.23757551e+02, 1.55739004e+01, -6.06923130e+00, 2.21427739e+01, 3.27699589e+01,-4.24698233e+01, -3.53840790e+01, 4.28685005e+00, 5.76211982e+00,-2.03864264e+01, -3.97015783e+00,-3.57065100e+00,-2.95853733e+01,-8.43158914e+00, 4.12817902e-01, 6.22469631e+00,-3.93123309e+01,-8.91883662e+00, -3.87482573e+01,-3.02226163e+01, 3.62726301e+00,-1.23861594e+01, -5.34873203e+00, 1.39279044e+01, 1.92503449e+01, 6.49587815e+00, 2.31146120e+01,-1.12406947e+01, 9.01421565e+00,-1.47388145e+01, 7.37121532e+00,-5.08421371e-01,-2.94205784e+00,-5.75777864e+00, 2.27445180e+01, 4.02633597e+00, 7.52726083e+00, 1.76493033e+01, 2.39532426e+00, 4.21788197e+00,-6.84085469e+00,-2.32120407e+00, 3.39449123e-01,-9.59746445e+00]] )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 235,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:08:09.598325Z",
|
|
"start_time": "2024-04-13T14:07:58.716562Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train_digits_224_1 = train_digits[:500]\n",
|
|
"output_centroids_w_pca_224_1, output_find_kmeans_w_pca_224_1 = find_kmeans_clusters_w_pca(X_train_digits_224_1, 10)\n",
|
|
"# Public test case 1\n",
|
|
"assert type(output_find_kmeans_w_pca_224_1) == type(PCA())\n",
|
|
"\n",
|
|
"# Public test case 2\n",
|
|
"diff_find_kmeans_w_pca = np.abs(expected_centroids_w_pca_224_1 - output_centroids_w_pca_224_1)\n",
|
|
"assert np.all(diff_find_kmeans_w_pca < 0.00001)\n",
|
|
"\n",
|
|
"X_train_digits_224_2 = train_digits[-500:]\n",
|
|
"output_centroids_w_pca_224_2, output_find_kmeans_w_pca_224_2 = find_kmeans_clusters_w_pca(X_train_digits_224_2, 10)\n",
|
|
"\n",
|
|
"# Public test case 3\n",
|
|
"diff_find_kmeans_w_pca_1 = np.abs(expected_centroids_w_pca_224_2 - output_centroids_w_pca_224_2)\n",
|
|
"assert np.all(diff_find_kmeans_w_pca_1 < 0.00001)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.2.5: Mapping Clusters to Labels (with PCA)\n",
|
|
"\n",
|
|
"In this task, you are to find `cluster_w_pca_to_digit` (a 1D NumPy array) such that\n",
|
|
"`cluster_w_pca_to_digit[i]` indicates which digit the `i`th\n",
|
|
"cluster represents, **when the clusters are obtained using the default values**\n",
|
|
"for `threshold`, `n_init`, `random_state` and `n_components`. For instance, if sample points that\n",
|
|
"are assigned to the 5th cluster are meant to be the digit 0,\n",
|
|
"`cluster_w_pca_to_digit[5]` should return 0.\n",
|
|
"\n",
|
|
"On Coursemology, in addition to stating the value of `cluster_w_pca_to_digit`, **please also describe\n",
|
|
"how you found it**.\n",
|
|
"\n",
|
|
"**NOTE: you may find the helper function `display_image` helpful here**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"centroids3, pca3 = find_kmeans_clusters_w_pca(train_digits, 10)\n",
|
|
"approximated_digits3 = pca3.inverse_transform(centroids3)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:08:50.413446Z",
|
|
"start_time": "2024-04-13T14:08:38.281506Z"
|
|
}
|
|
},
|
|
"execution_count": 237
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 238,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:09:24.555208Z",
|
|
"start_time": "2024-04-13T14:09:24.279190Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 10 Axes>",
|
|
"image/png": 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qY8wcorcLr9PO/E/pl6Uf05DSK4VOP0jXXnst7OIwo6Xz9Fb2Ndo5+a+0Pf8CDZmntNdhm/lhtHTeGjPb8y9gzLiIMXOI3i6+QTsLL9H2wo9oyIRtFhpzXnyk02las2YNPfvssxVf/8Y3vkFPP/00Pfvss/Tmm29Sd3c33XHHHTQxMVHxfFAbSlSkqNFKq3w3Vnz9FE3Vo/nmN78JuzhMSRUp6mmlVYGbKr5+UsE280FJYcy4lan5rIVWeddVfB1jpvGZs+xy11130V133VXxNaUUPfPMM/TEE0/QvffeS0RE3//+96mrq4teeOEFeuCBB2a8J5fLUU64qJPJyu6kcozy4j3T2GSQnJERUHiRigV2i51IcCbU5Ai781s5wSk1nWMXlnSRZdrFDm+bZZ0nzy8ERGbB0JgoanZBd6kqu+Jlwn3WYXRTh7ebKqGUotNqqhjU3XffTbFY7JJ2IZqjbezcpDa7+G13dxORN1PZ/W2KHdc7zy+32mePclRL81G2QfsoX0cWIBtdy3LBmY4Wqx32slu7kGH3c5OYz/zJMpdqWkgtsmCZcCdf0jZmHWxj5x62Q7pYheu1fBe/EWC5zFzKbvkLq/k9/1f/Tqvd4uGxsmN4pdWOfMDXaT4rss+O83cqCalUhUV0WkjcX3GW7ymjXTQJZqrdQV3U4V1U/q6pc5wYMwK7+Uw+R54kR5YYk2VTt5Se7WQXeY6U1qQUILKRSmnaCPN9tPp5PvKI0COzJCL5hMolxzoREQl5Us5hStionRZTu7GYKlG3MWOHzViyK8BoGPr8pUXuyMhMeS2b361ijI9/rOlUxXMMIYn5J0QEjZDrzbyN7DgbUmqxiYa7Emq64fTYsWM0ODhImzdvto4Fg0HauHEj7dmzp+J7tm3bRvF43Pqvt7e34nng8slQmvKU045dyi5EsI0TTNkmqx2DbeYfjBn3gjGzMKjp4mNwcJCIiLq6urTjXV1d1mvlPP7445RIJKz/Tp2qvLoDl0/5QJ1mNrsQwTZOkFewjRvBmHEvGDMLg7pEu2jRJzTlJis/Nk0wGKRgMFjxtdmw2x0uCxXJ3eGenO5qkm7BYk64lmWNMy9/Rj7G959ayi5jJdxoqSXclkWZZKEgTWoZEVEzg+wiM4R7lagsmZiWdOjKkr3MZheiy7eNlvBMuvtyQq5K8vf1BfTH0Jtnd7t3ktfHg2dYEvON8DkdHLBCsRPcV74Uf17qKj2R2TRNPj4nXRQJ48b5noIJ/j7+CT0SyRCSmCnsJF3IMyQppS65m79utqkmikC6mctczkaUo1omruZ29KNcYOxPmzgB3GuZJVZ74ARLT4vOiCgVIV2WYmyDXAvbONvCY9QUgWvSw63K+ytXOapCMtdxUze72CGidMw0P2sz7kHKNtJmIvrPCIrIMynNxNiO+S6eAFNL+ZxlSzjSbGmAoy7eSnHUUnGcrx8Qxf+MSd2DVB4NwrdtJ6VXPlyOI7bR5Aeb5GPl0S5SajFsfnLFvG6KpGyeNu67VcFzVnuwyMXk/CJqMjgkpJak0Ivlb8VsxS1t5gfbcXIFUUk19Xx0d09NLuWrz6GhoRneEOAcAQpVPA67zD8BA7ZxIxgz7gVjZmFQ08VHf38/dXd30/bt261j+Xyedu7cSRs2bKjlR4E5EKYmCpC+4odd3MGUbfTJFLaZfzBm3AvGzMJgzrJLKpWi999/3/r3sWPHaN++fdTW1kZ9fX30yCOP0JNPPkkrVqygFStW0JNPPkmRSITuv//+mt64bYIkmUxFuF29ab3ug18k9fKMsR8338Jd0rGI3VaJML///MryhPhTGH5xTyKplX+Erxk5y+dLicA7NG61NXcZka3LTLrCiqpAGWK5JqPSNEFj5KcAhYwmWmosp6PqAL3yyiu0Zs2a+tml7B6lDEYiUkdGUXjyvMOeiMgQ38uflrUpRAKk8+xqjJ7i6/oTYie9cC3nm/n85sXcT0tDY1b7jWGOoAmO8ntDo0K+m9RlF5UVn1eonDCpZBRn2kaNkY8CFDaaaKlnOR0133XENnPFKI+ciLNbPnE12/B/9HMysZDBx/9z9GNW2z/I40bKJZOilk8pKKTLOLdL4hGRkqk/LVza5QmTbCKtpo8XVYEmFY+1eR0zMlrCY5f8TTx75fKCX2hRJZGYTEoAwpYqzDJksVMki1vOC670Cv68z3SwlDYhjPHWCG/aDA3xPBcS9WJkPSqisuRVEjEnFEt5yqgGHTPl8pFN3Rc5Txqi3k0pxO2uNo7IudrP7TNFlqCllO8d5z4zhWQn+9xO9nKaOS8+fv/739OnPvUp699btmwhIqK/+Zu/oe9973v0ta99jTKZDH35y1+msbExuvnmm+nVV1+laDRqd0lQA5I0Rm+ZO6x/H1H7iBTRYuMqut64mXppJR2lA/Too4/S+Pg47OIgSTVKe0tsm8Pm20TmlG1W+z5OfcZKOkrvwjYOk1SjtBdjxpUk1SjtLf6X9W+MmYXHnBcfmzZtsl+50tRGqK1bt9LWrVuv5L7AHGkzOunT3vtsXzcMg0gRHT58mGIiLTaoP22eLrrD85e2r09vkoNtnKXN00WfJowZN9Lm6aI7Ahe9GBU2NWLMND4LorYLmTJRDR+WLnHPxKR8B0WGOZf+5HlRwr2Fj/f2s0Zyx5JDVjvu4x3FiSK7II9Ptlvt353gXeCBo3z96GlRyvo0u/zN0XG+7/Kd+S5xk10WsiyzjETS2vrk4svK0uEkzmP3oqyxUoiJ2jl+tl8+xu7O8VV8nb+46l2rvTgwbrXPjfMkJiORAgnhxs7oO/dNG6nFble87Y7++UYmFpP36NdrIpXa2F2fXsbf9+bIB1b7D3mWV94aXGq1fUIimRRlQ4pNfLwYEe5hn0hg5efjPiHFBcb5vWZZ1JRHq1FjU8OlmrodDqNHFnAfGzJsrixqQ0ayGLK+lWiruIhqWcR2nOjja41+jPtj3crjVrtLuPx3jbE8eeYUz3mtg8JGE8Llny0LjTUvP0JiXpjjM1IeGWJI1UXIKzMkzYuYfrbz4ibu9y4v22m8xPNccEx8XqZyGLJdwrDZarvox2s/Ntwx2gAAAADwoQGLDwAAAAA4ysKQXSTS3S0iX4wJvV5K6Dy7HaPCPV+Ispt5uJPdlIs7E1b7k5HDVjut+Pw3A1db7SNjXDMi7WUXmTcrXPMJ3pms1Vcok1nms9R3TZG7rwtSytB3w/sybENfWuyA527U6k6ku8QufrGzPNPJ/dZ3wxmr/d9b3rTaP0v+kdXOnWZ7t17ge/CmhNRS7tZUMkpB1npoAKlFIt2qog+NgC67ZDs4xDEsooZCBttzR+o6q50cY6P5YiJpXyu3S3GWrrwRUeNIlGj3CQkmPS7qj5wTCf/8ZX9LiXGkJZ+ysZNrEDKy5gY32BZGWA81lcnfzLiIZGnm/plczG77iT628cT1PP5uWcWRjDfHj1nt41mWVw4OcbK4wKCI5BsRY2ZCJNzL6eNbu28bN7+iBpvzZku2Jedv36XngpIIplwW4YRuQWH/IxnOZxJIV5YXtXEsb1XU4NHDqZzFhSMPAAAAAAsZLD4AAAAA4CiNK7tou29tXF7C3aUm9Hop3vPs24oGuRuKYXbpjnRx/vwj3bw9/9ogR8EUiF1bi3ycsOiGTi5a9Gpvi9VOn+LPDR1nVymJ8s4zagMI95nbd+oTkS59qcpuRkNEicg2EZE/zf8OJrWt4lbLFK5JWXenIFz7patYIvk/en9ltbu8LBH8IcG1R5pP8PUjokaCZ4Lbqjh3N6WeQMqdEox2X/J+Q7p7P9vG9uiO8/N+NM/j49ejV4s3iJosPlG/IiISLPnkM833EQjwc9AT4/FxIcD2y4Q7Zn6ZBkST7MScYvhFbZaAiGgps0upheeSbI+I5Ovg9ydW8vlNohbPV6/5rdVeGzpptfdnOYHYgQSXt0+P8RwZFdFGvklhUxkFVh7dIiM+ZDSOFi0ma1jRwsFTeZ5WPiH9i+iv7mCi0ul0erKFL1mU41VEQclaU1KCFM8UmWUJ0LQovdrXc5G46BcLAAAAAB8GsPgAAAAAgKM0ruxih5QftPovZREkKY5+8Y3z7vDQGLutAue5e355/Fqr/bsIJxAL+di92BlhN7TcpbxyBcs0RxPsymwa5B3koTF2r5XGdBnCzm3vyp365di47uSubCOv193xjbHMEQ6xW9Cb47asASLdlCURBdPVMW61bw9zbYoTIjHc20fYlr3HuN/9IyIp3aSQXSb1ZHVaErVGiEqqQp6TyY9UpEx2aef+vSbMz7ss8f3BKD/X/oSwX0Y8uxNSQhPSTID7cHIxt5vb2YWc8on6I0J+M8r7X35X6e42ZCSS++ynlWAXUounieUUs0VPI55dzK8l+3jeSnOON4qs5qSGD67YZbX/pImj9/bnWcY6kmEpLZETz0FB1FuSOfeEcqekXNTcRHZoCdFkMrK0SBxp++755XLmYi26RySKM6Pcv7kWPsdvcD+cK/LWgdMTLVZbqpZmlPvaI6KMlJhjDSmD+XXZRZNnhN6ljQ2b39i50gC/XgAAAABYSGDxAQAAAABHWXiyi2Q2F7NNuW1T9Ignz+6v/AneTZ6Y4BogaRFEc7aNXVMD141b7VuWcLKesdXs8h87wYnIet7naxpJPTLHrgSy7vZzqXPSzi0nvpPK6fVSPEmWNkLCTelPsZuyGGZ3Ya6VjTbZzcfXtp+22ot9bL9nLqy12rH97NZuOspSmTHKMpiZFlJLuXxnk2SsEdDuV0oRwtVvxsLyLVQQAVpRP7vJh/L8/E4KO4VEue/guIg+MypLZVkeEhQIsas45GVJLF9iG/tE7kAjX1YTye7Zk9/bLZEUsv+F7CWjWpRwqRc6RMY9IkpcxQmoJq7m7x3sZ2ns9qUsr6wSEXtSanljgosgHU6y7JLKsk2NkrCdDK4QyRp94v783rJxURJzlZQD5JxgExXiJmyj2LzeCmfPRI6zYpQ7stDM102V2P5/yLOcOTLGsltPyWbu99n8vGsSpG4bKQuZdf5Ncb+FAQAAALCgwOIDAAAAAI6y8GQX4Wo1vKIuRVlpcEMkEyvGuZ2LiZ3+YhtxYJyPx47xZ0QG2W2Y7eDPOB/h3f+pLnavrWo7b7V/u5jdnaU29mcbZ8vWhHbJxGqU7MUxxL1ru6rLol1UWiT4ksnI0uya9LSK8uBxn2hzn9waYzfzabFT/MX31lrtJQP82Z7hcattimgorSZNqTxhUmXpoiFsI139MvGQqBsi3cFEuix5dIKf33wTv2CKqCSPkDVkZIRMOJZtFn24jCWuTyw5XvG2Ry6wy7nrHPezlOuIiEyZ6ErKZW6VKKcRc5Uhoo2K7UL67dftIqWW0NUstdzcw1Fe10VYajkupJbfp/qt9tEUHx/PijmyKBIdeoXtFgkpTchFpohICkX1nxnfJNvCn+CHwislGBlhlrevDeMI1SRw1GSzMtlFRsQVxXwm+qsYkRFf/NaxAstXB7OcELGUFn1azeMs70nMZ+USkZzf6i3rw/MBAAAAAEfB4gMAAAAAjrLwZBe70uBltRBUG8si2UUiwYtIoiTdi4ERfm/TWXZbBYbYPa987A72ZoUbWvE9hUVdkVJY1CFpYlervzwpj5AAZrj9FwBaGWgiMgoiOY4oLy7Lu5dC3L9SKvP2cF/1+Th65edpLmzhO8SuzMhJrnGhZolqsfDY7w5XcinvliiKKpHJnmR9iFJQ//vEK/JAnRhurXgtfzPbL9Ml6ybxtQoxfo47PjJstb9wFdcZafeyVPbtk7dZ7dAAj9emQXFD5aXb7aSWaupXOI1MLCaTvAlb5Fq5nenSn8NgP9e+ubt/v9Ve13Tcajd5WOL4QNTikYmsQmJ+8okEbD6fGIcRlg4KHu6/YkTYt5nb2VH9Z6ZZyMreLF/X2wARLhItwkWrjaTP0XJ+88haNk0sa+WjQnYJ8vnjQnYpKCGRmCIqxWcTZWc3h0mpxe4coron45uTtbdt20br16+naDRKnZ2ddM8999DAwIB2jlKKtm7dSj09PRQOh2nTpk104MCBmtwssOeYeZB+V3qVdpR+QjtLL9EfSrsprZIzztu2bRts4yDHSgfpt8VX6bXCv9HrhZ/SvuKuGXaZnpyuvfZa2MVBjpUwZtzIB2O/pV+nXqZfJn5AO5IvYMwsUOa0+Ni5cyc99NBD9Jvf/Ia2b99OxWKRNm/eTOk0/7X5jW98g55++ml69tln6c0336Tu7m664447aGJiYpYrgytlXA3TUmMFrfd8mm7wbCRFJr1t7qSS0nMffOtb34JtHGRMDVGvZznd5LuD1vk2kSJFbxVf1+xyUk1tjP3mN78JuzjImBrCmHEhY5lT1Bf4CH28+bO0rulOjJkFypxkl1/84hfav59//nnq7OykvXv30m233UZKKXrmmWfoiSeeoHvvvZeIiL7//e9TV1cXvfDCC/TAAw/U7s5t0OoiaLKLvju8EGPXbbaFzyuK3D2y2rBh450qxYQrtEOURV7E7svFIU5YdSrDrmpvTrjObBILEREp6U4u6Qm5pvkj70bt39d5bqI3zJ9RkkaplTqtvxQeffTRebONHgEySyIeaTex89+MCjdlnI9LF3SvqOeSVXzOj8/caLVjx0RyoCQvnE1V2Z1YLgvZvlah/sEN/tu173299ybaWXyJkmqUWo0pu5w23yciorvvvptisZjzdiEiEtEu0tVv+vW/T3xC5cic58EyEuZndHkXa5RmZ2WX8G0d71vtz8f3Wm2Zj+r/GfqU1T65n0u697zHgzFwhseWynCEBFGZRFlBavkjz0btuGvGjE10hSnqGeVb9Gfyxm6OZLmnhftztZ/PGzXZRh7i7z0pwiuG8xxRU1L8eTJxnBK1XbR6LkG+ppTrzFl+ZTx5MbFejE66cdE9ZI5yHZrrfR+nnYUXKUnj1Eodzo8Zu2jDKtEkzYiIIGrifi8FpNzPnzecY3ukSyIMRt6eGDRaojYR1aLJw/K95XObJh+JSCa3RbskElMDv62tjYiIjh07RoODg7R582brnGAwSBs3bqQ9e/ZUvEYul6NkMqn9B66cIk09eH6aemCzNLWf4fbbb7fOgW2cx7KLMWWXDKUpT1ntnEvZhQi2qQcYM+6k3C4YMwuDy158KKVoy5YtdOutt9Lq1auJiGhwcJCIiLq6urRzu7q6rNfK2bZtG8Xjceu/3t7eiueB6lFK0WFzH7VQBzUbLURE1mDt7OzUzoVtnEMpRQOlt6nFEHZR2YrnzmYXItim1mDMuBOlFA0U36IWYxE1e1qICGNmoXDZ0S4PP/wwvfPOO7R79+4Zrxll+eKVUjOOTfP444/Tli1brH8nk8m5PxTSFaaV0RauppDusiqKpEiyXkWhWbgOo+zCSouuUh4ZDcDt1Cp2a27+KG9+kjvOfz3ECX0C43x/3qzQmWdx81fDgHqLUjRON3r+eMZrjttG+3AhLRlSHtPXwFJqUTE2jkwGlxcRLtl27q+rmjnC5a3MVVb76FFeEF9zmqWrcle9dfxKk1JVSDJ2yNxLKTVO632fvvTbZ7ELUR1sI7+jlADLI5G0pGF8fwVRb6UjxFEqt7cestobw0etdr+f7XpM5Dz6v8/dabV3vPExq71kN39wdP+Q1VZjsgZPuewibvYSSd8G1NvzO2Zk/8sEfCJRmicv5MIyGbgoQq2aDH5PxMNj5nSJO/p4gYvoHEqxpLV/qMdqj5/lej2+BNvXL1Rgb5b7wSe6PyDq+ITG9ZsNn+fFgyfBEWZyLE4n43qv8LupMeO/gy6FI2NGk+/EOJHhbWU1VbSkY1LSDIqISJEDU9bOGUrzOIkHud+Movhtk1+5gvRLRNXXM5ot+sX6wNpEXF7W4uMrX/kKvfzyy/TGG2/Q0qVLrePd3d1ENOUBWbyYH+ihoaEZ3pBpgsEgBYPBiq+BuXPI3EvD6gzd6LmdQgZr8gGa2kdy/vx5WrmSw05hG2c4VNpLw+YZWu/7Y90uRqji+bPZhQi2qSWHzLdpmM5izLiMQ4U3abh0htb7P40xswCZk+yilKKHH36YXnzxRXrttdeov79fe72/v5+6u7tp+/bt1rF8Pk87d+6kDRs21OaOQUWUUtbCY53nUxQ2mrXXQzQ1eHfs2GEdg23qj1KKDpX20pB5mtb5bp9hlzA1WT9y08AuzjA1Zt6iYTpN64yNGDMuQSlFhwpv0lDpFK0L/DHGzAJlTouPhx56iP7lX/6FXnjhBYpGozQ4OEiDg4OUueguMwyDHnnkEXryySfppz/9Kb377rv0xS9+kSKRCN1///11+QJgigG1lwbVCVrt+Th5yUc5laGcyljhadPuyKeffhq2cZBD5l46Zx6n1d5PkM/GLks9y4mI6JVXXoFdHGRAvU2DdJJWGx8nL/kxZlzCe6nddK50jFYHbiGfUdkuGDONz5xkl3/8x38kIqJNmzZpx59//nn64he/SEREX/va1yiTydCXv/xlGhsbo5tvvpleffVVikajVC8MT2WdTyuSU6ZdyxDCUojfX2pizatnGWe/7PoIx4+HvKypXtvMheI2NrO+3eJh/fJnyT+y2mdOtFvtxSdFBsEh3nmtJlgzJyorrGSTnfG0+oCIiPaa/FcaEdF1xk3UY7CH6ktf+pKjttGQdpI6aJkrVDWzi9WMVQ6vTXeLdfMS1o2jftZF30r2We3QaZFB9oLIBSCKWWn9bKd9zhZqV0ELnQ4J3Ft6TTt+vfcm6jGuJiKiPmMlHaV36dFHH6Xx8XHn7CKfJVHwypPg5y8wGtbeEmplu+VauC/Sw2yzwyF9g+Y0+9Jsj8Es7yf47WF+PmP7+Fm4+nccBu0/yVlQzXERXivuW5XbTGTHJbkX4KINT9PFMaNev3iBqf/Ny5ixsYWR4ec5dI6f89gH3H9ERG928f0+4+c9RR9rPm21903w/oY3B7k9cZa/S+QU/yS0D/P84k9z2yv2nvgmRWG/kjie4nHlTenpATwp/k4yq7C6WEzuVPYgERHtzf9Se9/13puoxzPPY8YGLRRV2I+IyAiI/YYyi6v4TTLE1OER+2hGE5ztejLsr3iOtyDD/W3Cy0uVn6/yec7JjL9zWnzMlu9gGsMwaOvWrbR169bLvSdwGXzae19V5z3++OO0bdu2Ot8NmOYO/19e8pzpv7APHz5MsVjsEmeDWoEx4042N/2PS1ayxZhpfBormT4AAAAAGp6FV1hOIlxQRlZfSQcS/O/gGHeDd5LXYxE/uw7/qpsLXn0yfM5qd3rZLXYgz1LL/3vhFqv94ts3WO1Fv+bPanmXw0LVeZEVUhY4a3Rswmul+7E8PM4U4WjFZnZZZoXLP9vBXrhFrSyj5EU6xf1DHHHVdEa4OCdFqK1dSK1dmFq5W7JGYWfzgeZizXKfmGLc+EknXmLZ0JdlqSUzyP2ePsKyy++FBCNDMZvP8GesPMryivfYSb4/kSrbtCmGpUQoqiazNDDyO8nwU+8Iy01tB/UMwZ4Cz0O/GVhjtX8V5HZA5NQKi1DY1jGRMXacP8+XFKGdWZEtU0iVmptfuvZFds1yGUI+dyonxmJBP8+VyHnBLvNpuTQr5w+Rrdqb4T4KX+B5Ln+Gx1I2x3bN+nm8NQ3zNYNjwk4ZMY7FZ9lJylXLLHWY5+D5AAAAAICjYPEBAAAAAEdZELKLdB0Znsq7es2Ensffz95dastzsTd/mt1cZ8Z4R/j/6uPNaZF2lkW8oghQ6gxvfIq+z27Rqw6yyyt8lKNjaHSc71W4vWe4uC6jmJFrkDuuRSo+w5THy7JoSrlM7giXbZEFcDTJNvttkSMqUkfjVnvJiMgaKSQ4KbuUZ1q1uz/9Zm3csA2GKoqoH9EnZkZPZe0ZG7fasRMi+2ITu4RVWEQvFUX/SjdwQkgqKZZdSuI+DJ8QfWyKxOmub/vslg2FHDNZKRGK6DgpaxBRxzkRUSFd6eLZVdlsxeNaATGbSAglxqQpbWFTyFMyMwpJ3l+DjZnLKDKnFTgUNvAOcvG8JhERFDkhsmkHKv9EG9nK0WmmGJ9KymM2UZLz2f8N/KsGAAAAgEYEiw8AAAAAOMqCkF0kmkvJtE+mUhLuTGOU3V/xD9hl3PIbdueTX7jCgsIdLKM2JrmiokqJiBW5o1vsOp41QdJCxMa9q+2eJyIjKZJLCbd9rMgJhJQomJVOsfs/6+F2x2n+vKYT7JpUVUQTKRu39MwTG8xtXA2zfCdTjhu5g15IiLbRQVrSI1n0rXL/2o0JLalgtZJkg9pJS5RIQhqbLCuIODmzKBuRbi+7pGtSLqkq+sGmL1WVtrBLCtlwVCnB2EaaSNskJyqeYydPSyuV7MaYgwnDLgd4PgAAAADgKFh8AAAAAMBRFpzsYsdMF5Rwf0n3rnSFyQgZ4c7SdnXbuNvmLKM0ckRLtUg3pcztNanLIIaQqYwkP6I+ESHRPswSTEsLR1pIvOPiuhdYWiv/PH7BHbvA5wW756+8HzS3rnxPqWLT9lra58noFRsbiPMvy53cqONLq/lSuVbHbEiJQ5G34vF6s2BkltnQJMWy1+S/q/hdqItc4sL5rEFHJAAAAAAaFSw+AAAAAOAoHxrZ5Yqxdft+CKJU6k15WWe5I1zuFJdyyQWui2PY1GEx7dyXLnRBupZayhXVXGuhJAqrN5fxDFcjf1QnkVROJgYuMottZkgyH2Lg+QAAAACAo7jO8zEdy1xUhUucCebCdH/Omir8ErjXNjLFufjLze67uszzsbBtUwVV2cP5v5M+9HZxMbCNO5mLXVy3+Ji4WEZ7t3pFz6QCasLExATF4/FLn2jzXiKiXaWXa3lL4CK1sM2CHTfz+J0wZtxLTcYM/cfCHDPzSDV2MdSVLB3rgGmadPbsWVJKUV9fH506dYpisdil37gASCaT1NvbW5fvrJSiiYkJ6unpIY/n8v6KhG3cbZuBgQG67rrrYJcagTFzZTSCbT6MY4aofraZi11c5/nweDy0dOlSSiancmzEYrEP1UNBVL/vfLl/IUwD27jbNkuWLCEi2KWWYMxcOW62zYd5zBDV53tXaxdsOAUAAACAo2DxAQAAAABHce3iIxgM0te//nUKBoOXPnmB0CjfuVHus5Y0wnduhHusNY3ynRvlPmtJI3znRrjHeuCG7+26DacAAAAAWNi41vMBAAAAgIUJFh8AAAAAcBQsPgAAAADgKFh8AAAAAMBRXLn4eO6556i/v59CoRCtW7eOdu3aNd+3VDO2bdtG69evp2g0Sp2dnXTPPffQwMCAdo5SirZu3Uo9PT0UDodp06ZNdODAgXm6Yx3YBrZxGtjFvcA27sX1tlEu40c/+pHy+/3qu9/9rjp48KD66le/qpqamtSJEyfm+9Zqwp133qmef/559e6776p9+/apz3zmM6qvr0+lUinrnKeeekpFo1H1k5/8RO3fv1/dd999avHixSqZTM7jncM2SsE28wHs4l5gG/fidtu4bvFx0003qQcffFA7tmrVKvXYY4/N0x3Vl6GhIUVEaufOnUoppUzTVN3d3eqpp56yzslmsyoej6t/+qd/mq/bVErBNrCNO4Bd3Ats417cZhtXyS75fJ727t1Lmzdv1o5v3ryZ9uzZM093VV8SiQQREbW1tRER0bFjx2hwcFDrg2AwSBs3bpzXPoBtYBu3ALu4F9jGvbjNNq5afIyMjFCpVKKuri7teFdXFw0ODs7TXdUPpRRt2bKFbr31Vlq9ejURkfU93dYHsA1s4wZgF/cC27gXN9rGdVVtiYgMw9D+rZSacWwh8PDDD9M777xDu3fvnvGaW/vArfdVa2AbdwK7uBfYxr240Tau8nx0dHSQ1+udseoaGhqasTprdL7yla/Qyy+/TDt27KClS5dax7u7u4mIXNcHsA1sM9/ALu4FtnEvbrWNqxYfgUCA1q1bR9u3b9eOb9++nTZs2DBPd1VblFL08MMP04svvkivvfYa9ff3a6/39/dTd3e31gf5fJ527tw5r30A28A28wXs4l5gG/fietvUfUvrHJkOf/rnf/5ndfDgQfXII4+opqYmdfz48fm+tZrwpS99ScXjcfX666+rc+fOWf9NTk5a5zz11FMqHo+rF198Ue3fv199/vOfd1VoGmwD2zgJ7OJeYBv34nbbuG7xoZRS3/rWt9SyZctUIBBQN9xwgxUatBAgoor/Pf/889Y5pmmqr3/966q7u1sFg0F12223qf3798/fTQtgG9jGaWAX9wLbuBe328a4eJMAAAAAAI7gqj0fAAAAAFj4YPEBAAAAAEfB4gMAAAAAjoLFBwAAAAAcBYsPAAAAADgKFh8AAAAAcBQsPgAAAADgKFh8AAAAAMBRsPgAAAAAgKNg8QEAAAAAR8HiAwAAAACOgsUHAAAAABwFiw8AAAAAOAoWHwAAAABwFCw+AAAAAOAoWHwAAAAAwFGw+AAAAACAo2DxAQAAAABHweIDAAAAAI6CxQcAAAAAHAWLDwAAAAA4ChYfAAAAAHAULD4AAAAA4ChYfAAAAADAUbD4AAAAAICjYPEBAAAAAEfB4gMAAAAAjoLFBwAAAAAcBYsPAAAAADgKFh8AAAAAcBQsPgAAAADgKFh8AAAAAMBRsPgAAAAAgKNg8QEAAAAAR8HiAwAAAACOgsUHAAAAABwFiw8AAAAAOAoWHwAAAABwFCw+AAAAAOAoWHwAAAAAwFGw+AAAAACAo2DxAQAAAABHweIDAAAAAI6CxQcAAAAAHAWLDwAAAAA4ChYfAAAAAHAULD4AAAAA4ChYfAAAAADAUbD4AAAAAICjYPEBAAAAAEfB4gMAAAAAjoLFBwAAAAAcBYsPAAAAADgKFh8AAAAAcBQsPgAAAADgKFh8AAAAAMBRsPgAAAAAgKNg8QEAAAAAR8HiAwAAAACOgsUHAAAAABwFiw8AAAAAOAoWHwAAAABwFCw+AAAAAOAoWHwAAAAAwFGw+AAAAACAo2DxAQAAAABHweIDAAAAAI6CxQcAAAAAHAWLDwAAAAA4ChYfAAAAAHAULD4AAAAA4ChYfAAAAADAUbD4AAAAAICjYPEBAAAAAEfB4gMAAAAAjoLFBwAAAAAcBYsPAAAAADgKFh8AAAAAcBQsPgAAAADgKFh8AAAAAMBRsPgAAAAAgKNg8QEAAAAAR8HiAwAAAACOgsUHAAAAABwFiw8AAAAAOAoWHwAAAABwFCw+AAAAAOAoWHwAAAAAwFGw+AAAAACAo2DxAQAAAABHweIDAAAAAI6CxQcAAAAAHKVui4/nnnuO+vv7KRQK0bp162jXrl31+igwB2AX9wLbuBfYxp3ALo2Lrx4X/fGPf0yPPPIIPffcc3TLLbfQt7/9bbrrrrvo4MGD1NfXN+t7TdOks2fPUjQaJcMw6nF7H0qUUvTDH/7wsu1CBNvUC9jGnSilaGJignbt2oX5zGVgzLiT6THT09NDHs8lfBuqDtx0003qwQcf1I6tWrVKPfbYYzPOzWazKpFIWP8dPHhQERH+q9N/f/3Xf12VXWAb2Ab/Tf23du1azGcu/Q9jxp3/nTp1qqINJDX3fOTzedq7dy899thj2vHNmzfTnj17Zpy/bds2+ru/+7sZx2+lz5DP8Nf69j605FWO9tB/0p133qkdt7MLkb1tbmv+77BNDcmbWdqd/klNbPNJ792wTY0oqgLtKr1M+/fvp7/927/VXpv7fPan5CPYpVbkKUd76Bc1GTO3Gp/FmKkRRVWg3eoVikajlzy35ouPkZERKpVK1NXVpR3v6uqiwcHBGec//vjjtGXLFuvfyWSSent7yWf46/tAGDXa7qLM2lynzuRUhoioarsQXco2gfrdrEBzh/p98gVul4QNSqVLXlMpdcnPsjunHuRokohqaRtMpLWkJvMZucAudtKCg896raj9fOaiMSN/U2r1O+UkiqqSseqy54No5ocrpSreUDAYpGAwWK/bAGVUaxci2MZpYBv3gvnMnWDMNC41X1Z1dHSQ1+udsfocGhqasUoFzuGnqUF3/vx57TjsMv/4DdjGzWA+cx+Yzxqfmns+AoEArVu3jrZv305//ud/bh3fvn07/dmf/VmtP252qnRZGR7hbjdVxePatTSpxXvJ68trlr1Q1f3VAo/hIVJEO3bsoL/6q7+yjs+LXYjI8Ir+LLdTgF2gRiRstUstzVY73x6y2t5sSbSL/N68aGcLVtuTy/NnibYq8DmGtJm0U5ktayHPeIypZ8gttrkspA3luLHrxwZi7dq1LpnPqozIkM9kNe8xbOY57ZrCdnbP/JVGjMxhLHku/t08r2Pmcp5n298ReV05ZoSMfCX9W41dL+f9V0BdZJctW7bQF77wBbrxxhvpE5/4BH3nO9+hkydP0oMPPliPjwNz4Ac/+AFt2LABdnEhsI07eeihh+iBBx7AfOZCMGYal7osPu677z66cOEC/f3f/z2dO3eOVq9eTT//+c9p2bJl9fg4MAe2bdsGu7gU2MadfO5zn6NMJgPbuBCMmcbFUE5u66+CZDJJ8XicNhn3XN4OZBv3kCahzHKedEPZySW2cowdNte0k3tme//lus+KqkCvq5cokUhQLBab/X5tmLbN7dG/uqxoF20jmOxDf9m12uJWM7eU2xO9fF5mkeg70SW+SW4Hktyn4QvsvgxeyPH54/wGIyvkmEyWry/kGCqwlFPO5Q6losrTaxM/rIltPuX7nHM7922kFsMrpEhTPPulKly85eO0Eg7JmEVVoB3Fn9TELpvoz67cLrO53YUttPnJW1kWNnzi706RDGpWOfQiqijGgIguU7Itx0m5XeYqPVSgqAr0Ov2sNrbx3OvYmDFs7KGNHxs7a+PH7neqlmPgMqSWoirQ6+aLVdmlAeN4AAAAANDIYPEBAAAAAEepW54PNzCb+1G6v6S7kEzpvhSuLeFqVErKLkJGkUmwbFzDnpCIMxf3YEj3dL5AGsrGDTebVON25HdvCmsv5RdxVMv4NSy1JK4V37ebZZFSnm3mP8fnKy/3my8nzkmJZ0G6MrMsx9hJLS5TKevPbK5XO1exHGvyuOw7O0nTzi0tx4e6dCK5hqaqCBXdLh4ZIdbcxO0YZ5ostXM7vYTH3MQSnreyHXzNUpht5E/yPTWd4eOxkzxmAudT/OYL41ZTJZLavWpzaTWmtHuG5hm73xdPOKSfF2f5odTB7dyiiNWeXMQ2yMf5uiXxc+ERU1JwnPshMsT9GTo7wedf4H5XWSEjp1lq1iQ0qjIys0aRL/B8AAAAAMBRsPgAAAAAgKM0ruwid3cLuUPu4jaE+0vb3U1EFBQRFlLmkG49Kc0It5VtxINwfdruMm9iV5sKiXsQrkVPIiXfQiqTEffBbk7KCZnApZ5oW3e8sIcqk12ynexrTPXy8aZrEla7rYldhycH2/iyk/x5kSF2FYZG2Wa+FEe1GHa29Ih7FWY1ZqsdI1yWmjxj2uxAv1TJaRcioyGMgHh+RdpqQ0qLsh9kNIToE7vICy3ypSjeK597mSQuL6KVylkAyc6kJGWUpQmXUovq4vEwsZyjxYbXct9G1o5a7c/0HbTan2g+UvGz/zDJ4at7Rq+22geOLLXa8XdYs2k/yBJP6OiIdi01wfObSqWttqnJze60kfytkfKK0dpitYuL9CiPiX62zcgafqZb1nC/bF5yyGp/JHzWakc9PPe/n+u22rtHr7Ha75xeYrUDB9n2rYf5nqLHuJ+9wzyPSlsQESkZ5SfnOpuoJo05yjGNN/sBAAAAoKHB4gMAAAAAjtKwsovmghTuX0PKKS3s/jKb9B3IxRj/uxjha5kBXo+VAkIKKYqoFuF1UsI778nzOd68cCuX+Li8vukX1y/wOb64fq/+QXaTkYfdZ7ob20VuZRmZ4Kmc/Ei62otNepKxbAu/J9/B3/EjrewqzpaEbJPg90fOcT+ERvm9vpSo2yJqvmju/KKMepJtWSujbL0ubeCinfhVYecmFXbylLv3Ra0dFWfXeqGT29lFbI9cVIwncSlTjC05PvzCCxwaE4nhxlhS8Y3yGDCG+ZmYIbs0ajSYfI6k/CfnPCGzEBGpxSx5JFbxvDd0I5+z9maWVL7Us8NqX+vn+SUkZNL3CzwPLQuyRBBZxLLX8uiw1f73ptVWuxhhebkj0Knda+R98T1KZsW2KrpHdpHzlkd8L6OVJa18T4vVHl+hy8gj6/k5vv0GlrjuX/Rrq91k8LNbIv68CyWO/FsWYBu0dfJA6Q5zhMvrgeX8Xj8/B/402zKcEXWuimUSipSh5TxOtQeeDwAAAAA4ChYfAAAAAHCUhpJdNKlF7roPCZkizm6qwpIWqz3Zqbv2M+38/kKM3VxFqXjIMgdiI7ZhF7xgEzghz5eSjUwa40/LpDF6kiFvUuyqFglitMiA+ZZaJHa1ImSES5h98PlW3bWfXsLfv6efXY1XNV+w2vsu8C57/xh/RjDJ/eBLs0EMOzeuspGrtDo04vzynd5SSrL7iMqHnaOaGixybMmIsRiPJyKiQg/vpk8tY/fy2Er+jOxSfrD9Md6tb4oEfmaR26ognpcs30dwmO+j+QQfbznK3yGQFDJkgt3PUxe2SYxUTW0Rl6C5/GXyKiF5ERGll7GdLnyU37Ns7RmrffeifXyOcOf/IM2u+iOTLJGczwhZzeT+bw3yHLSyechq/9m171jtl0prrHYipcsQ/gmWAwIZlnAMGdVXcpGN5NgQkVxmlL9XsZlD4vIt+vxtNPE8lDP5mf7u4EarffjCIqs9Pi4il8TYiLbzs97XMm61fWLikQkwlYdnnpKQ+2VCS/KW+R/knOCxGTNUm9BKeD4AAAAA4ChYfAAAAADAUbD4AAAAAICjuH/Ph41Wq4XXCh2u2MZaZnoxH09epa+z0tcIXbpZhOiJQnHFEdb0fElZmKxy4ScZSlhs4usU4kIj84pw3AnW18Ln+fqBVNma0Fd5jWibac5N+Cpnny1FOWQt3e3X3pLpZdt8tuuo1Q6JTTKnR1qsdvwUv7f5FOvGHpnJ1Ky8n0PuPTFEhloVFPckQjaN8qJ/suicyLZJBdFWwn522U7riU2Is5YZOCx0eTGeZCEsIqLxlWy3kXX8Xa772DGrHfNzlsQjY6xlj5zl0EQjI7TlZu7D1r4xq11cwueMRfm9vizfX+tZsR/Kr09nWuitzJY6nyG41RRJswuvFXvb8l1ldlku9lNdw2GYf9w5YLWziufMX164zmq/eZqzl+bOsX19Yh6SKQXeF/PZYD/fx21d71vtW5Z/YLV3j/JnERGFxvh7tA6JfSx+MebKx9k0Vv8Zjm2mklmatcy8BRHKn+FnODCuP4fGBe73X+VXWG1vgjs1epT7ukPs/5O/KemlLVb7QC/bKRLj8Zad4Dc0i2KAvoyYd2RIs8yYTWXZg2XYLQrLAQAAAKDRweIDAAAAAI7iftlFoIVtShe5cJ0XWrg9sZTPn/yIKAxHRGuu4hC0gJfdS++c7bHa/gS/v0m49oMJmcGTj+ej7ObKClddoYXPiXRwmFqxhd1uGWI3WtNZXdZRQnaRr2juwPkIH5QSggyvtSsmJ7LP5hexmz/Xpn/f5kUcUtbh5/DJQ6nFfJJwt0dPsf28F/RCSRbCJa/E/ZU6OJywFBGyUEh8H6ma5XSpy5ux+ezRcW6X5kFq0cLSRVtKEzJ7qbCNNp6ieoh6ain33TXXcwGs9a0nrPYbwyLL4tFWqx09zvfhy7BrOd0jxvIivv4N3aet9q+yfE72AxH+66scIkxUpSw5n2G31UgwAhXlEMz0Ej08PdXP3/WmXu63uI/nm18nuBjZrkPs/m8+yNeKnxOh6pnK/ZFezP18OtdltfcFeY5d13rSah+9lsNxiYhGR7hAWvNJHn/+Ib0AnSsRhdcMUQDUF+A+aRrUn8N8jJ/dUpift9AFtnnzaSEziUdyskNIoyVZaFGkiiiIMZDidiAhMgdPiAzPUirO6L+LUqpUdvJkjSSYOb/zjTfeoM9+9rPU09NDhmHQSy+9pN+XUrR161bq6emhcDhMmzZtogMHDlz2DYLqGFNDtK+0i94o/Yx+af4rDakz2uvTqb+vvfZa2MVhRouD9NbkL2nnxI/o1dT3aah4UnsdtpkfxswhervwOu3M/5S251+gIfO09jrsMn9gPlv4zHnxkU6nac2aNfTss89WfP0b3/gGPf300/Tss8/Sm2++Sd3d3XTHHXfQxMRExfNBbShRiZqNFlrlWVfx9VM0Vdfhm9/8JuziMCVVpKinlVaFPl7x9ZP5qXoPsI2zlKhIUaOVVvlurPj6SXWYiGCX+QDz2cJnzrLLXXfdRXfddVfF15RS9Mwzz9ATTzxB9957LxERff/736euri564YUX6IEHHriim5WFu4yi2IkrXK+5Fv5KmcXsHlrbL3QTIrqxhf/6fHXwI1a7dJRduu3v8ec1nRPFeITr3QzyZ/szwrUvXN3ZbnaXxSLs5gp6+TonEuz6VJ6y3dJiVzWJ762EO7/DWEwdxmKqhFKKTtPU7vO7776bYrFYTe0yK7Lwmog4yEe5f/Jx7R20NMYTSMTDbsDzWXbRhkZ43ezLiKgG8SyYTSJqI8R9WogKF367eHbifM2iqNslkjuSP61H5oRH2AbNfuHyFLv1F+WX0iL/xYysGZpyt1+UqZRSdKpwiIhqbBvhMlUyI2HBJsOpdPsLt6yUn4iIsj18rauj7CY/kubMmMcO8XPYsZff33xORB/JQo0mSzuplWyzjzbzX7vnOjmq4lxAyC6zSVp2xRYvuoo7PD3UYXRTJZRSdNqcit6oqV3s5BWjcgSdLOxXXMTP/0SfbpeWPs7+uzrKctjAJH+/Xx272mrH9vF12wZE5F+SbeRNikgIL9+fPyUycBpsu/fa+bPWtrAX6YYOfe59pa/dauc6+P0BEXHVkXTPfCbnWTn/0gTLLh6xDSAQ1ueI0BjPPVKOl3JuoZntmROFNSf6+Jz8ErZNKMq2kbKL3CoQGhfFSsdYflMiS3a5NGkrVdYowkVS0yseO3aMBgcHafPmzdaxYDBIGzdupD179lR8Ty6Xo2Qyqf0HakuG0pQnPaTqUnYhgm2cIKNSlFe67grbzD9TYwZ2cSOYzxYGNV18DA4OEhFRV1eXdryrq8t6rZxt27ZRPB63/uvt7a3lLQGiGZPoNLPZhQi2cYK8OVnxOGwzv5QvCKeBXeYfzGcLg7pEuxhlLkSl1Ixj0zz++OO0ZcsW69/JZFJ/KKS7VLpRZVu4xUoyN1Q7uxM/0cbJqso5eZbdgG2cJ4eaT7ObyzchCiAJ2UWFZBQFv9fbJhL0+Pn+lsU4iVLUx9c84eng88uWhJrsUsPEYrPZhagK23gqr101eUwmCxJu/kJE7NZu1l3n0p0vGUqzu90r5h/TJwoDtnLUUCnMtpns4gdjslMUQWsXSeWifB9mE/ezIew3mdO/c/6keOCUiBpIsUzgSaVprlyxbbRxI+5ZymDSNsIVa5js/i40ldlYFMlKFPi8t09zob+WA/ye+Aec9M0/zG5qJaJuvH3seg8G+Z6WB/mHZF+Yr39eFnkUEpEpXeJEWh/InfuG5/KjWq7YLnPEiLHUkurl/k7169/19k6WqPzCn//WCH+2d4Dlktb3uROD59n2nqyQxsbF/gkxdsNiPjL9LVZ7cglHoJ1fwc//yiZ9QRBp48/LtPJ5UVE4TxbU0+qYKWUrXdXNNvI5spl/DRE14snqtvHm+H7lXCULaI5HxThbymPmqi6W0/qa+LdjLM/Pwv7TS/izsiKx2KTNvcooydmirLTXap9krKaLj+7uKc1vcHCQFi9mvW5oaGiGN2SaYDBIwWCw4mugNgQoVPH4bHYhgm2cIOCJVDwO28wvAQNjxq1gPlsY1FR26e/vp+7ubtq+fbt1LJ/P086dO2nDhg21/CgwB8LURAHSBx3s4g7CRvOMHzrYZv6ZGjOwixvBfLYwmLPnI5VK0fvvszZx7Ngx2rdvH7W1tVFfXx898sgj9OSTT9KKFStoxYoV9OSTT1IkEqH777//yu/WLgmQSHYlE33JnPdxb0Z7y8FJTibmGRE7lVPCPVuqIvGPcA+aQenO5+MyadYnW4/w+UJf+aWHI258ubLPlTn2bRK/FFWBMsRu7QylaUKNk58CFDIitFRdQ0fpIL3yyiu0Zs2a2tqlSlSQH7eiKCVSatXdlB9pOme1x0TYyWiC21ER4EKaDdg9LKWWRD/3dWYZu5wDcZa+5GBQJl8zFBaJd5Tu1k3n2S0eELV6ms+KpF1eRZOlBH++maJk6QL5jSCFPc3U619FH+T3OWIbTRIrcD9Id7Ks81IM63+feIQEdTbFYUqF8/yeyLBIVJWsrM8XW/iHPdPBfXr9ovNW+6MBTk71L0JPlUn+tBogs9TN0dz40/dQPmZUmibUGPkoQGGjiZZ6ltNR89362UVKBMKVLSMnzHaWJdKL+ZzYYn6eiIiub+YIl6MZrqdz5nSb1e44wbYPnxaRGkkR/TDJ86Qp5EItMko8NxER4RUc5YRyUhZoi+myY3szf15S1OyRye2KVKKMkrZJ0YQam5f5TJPsRO0vksFiQvo3ZnkOCzFR8+tqkZTtKo6+vLOd85V8MsI1crKiwM5vMhy5NJrlefFUO4+ryUUiCibFknVA/J4Ysh4VEZGs9aLqWztszouP3//+9/SpT33K+ve0hvY3f/M39L3vfY++9rWvUSaToS9/+cs0NjZGN998M7366qsUjUbtLglqQJLG6C1zh/XvI+oPRES0mJbR9cZN1Esr6CgdpEcffZTGx8dhFwdJFIbpzdGXrH8P5H5HlCPq8S+n1eFPUl/gOvogvw+2cZikGqW9JR4zh823iUyixcZVtNr3ceozVtJRehd2mQeSapT2Fv/L+vcReoeILs5ntJ56aTnmswZnzouPTZs2zbpJxTAM2rp1K23duvVK7gvMkTajkz7tvW/qHxU8RIYxVQXy8OHDFIvFZrwO6kd7cAn9yeKHiIjITM+MbpneAAbbOEubp4vu8Pyl7euwy/zR5umiOwJTXgxVyM943SDYptFpqNouWq556UYVURdmgI+HAzZlmYlooiD0XOFKz4vkUt5F7Dr3TYqoFuGJzon6LFkR4TLZxy6rP13KrrNNkcNW+0iB3aOyPLQvqy8eDFkCWfveImGN+A519pZdGul2FFENZkjUOAjx/XpD+g1LOepUll25pSTbw5sXSbREP2Q6+DOSy4TUspzdiR2LeBd/LMSuz4mcSEomZJdokCe/XEn4Woko0873lBvmzaNFEWkT8M3zMLOJfJHPsSZKyERt+tclsyASIInS9obor3yzqHHUw3+Jyp3+qR6+cGo5u4HvaDtotXt8fP3JIvezRw5rm/pCRKTXFZJJ16TEVEGOcQz5R5xUYEL8vXMd/ExlOvn8tR16vZSIh5/vgSQnfAsf435rHWD5wzPEkRMqo0vSFW9V9pmsbyIT/ImvkzdFYi2lJ91qDvC9jstgMSHh0HzapVrs5JUyud4Qz578fYo0cT/0hFlG8xpC2izy+Il6uN/bvSxJ3bWYZZpXRSTXMT8HfZTERtt2xVJXoKjPvR5R28WUdV/qUPcIVW0BAAAA4ChYfAAAAADAURpKdtGwkWAMETiRTLO0MlLQNyK1BdgFGV7OLq+EcElNJtgN6ClyuyQUm3yLkERa2Y22dPGo1f5U/D2rvVzIEAfy0i1c+TtMvVY50ZrcgW4Wyt/kEsQ9SreqDBrx+fV7T5R4p3y6KFz7RSEt2Uhfk4tExFEX95uMfJJSSzrPbmn5vHi9om5OhPdpxIJ69EY2z37jiRi/3/RLP7qLXMh2ycekvCKeN19WdyGrLJ/oE31EHfzsj17P/TBxlYjc8Iqd/m383uUrOLppQ5iTAQYNlhzGs/xM+NMyeZi4B6+uEWnJlEQCMkMLU6i9O/mykPfh52cyHxc1iVrYRb68aVh7+6TJ4+TEMEuVradEWfVBnufs6nvY1vvx2kgiYj4r8W1TwMP9LSPWiIgSOX4+tOCx2RJeuQTbJGOm6MO8LvcHJti2oSEhN0b4N+nl5Mes9r/7V1ttmXRPjrfWCEtlK+L8LNzcfpzPWcs23uvn6BjD5P5vMzkaikivSSWTENZDyofnAwAAAACOgsUHAAAAABylcWUXiZAigkl2TY1fYPfS78f7tLd8NM5JeTYsOWa134twWejhJCdmkV6nkIiiaROREBE/H5d5+Bf5uHpi0BBueiEvGBmRZKhQ5uOyk1Tc5M6fJbGOhUxwJcv0mPr3KAlNJewVrj+fqMMSFi514RHOdIuohraZIXpERKeEW7o4KupJiHLzhXaWEaKdLLW0BvTIgOEAu5Qn5Avu9yDb1qxQItFQaFR/9oJDIpqolfsuGGI7FZbwly8JQ8sEbUE/f17Qy5/RYlN3ZSzNY6U7I8aHlgCqrKaUaGsSjHzFrk6FE3KMTZIxIygiqGJiXoiKpIk+/TmcEFpwQTzT4WHuWym1aNJJUURj5EQNKyG1GCJiy4iwLXKL+PnPt/B1QsKmBaXLYePClnI4yRpWtqXd5xs5ZoriGdGigcoq7g5zv7d8wP0VGpUyNMtmMpLPUELKFdFi43Gew3Ys49+sjuu4LtYtXSxhZlfxuH0vs8xq+yf1TLFtoywFGUme0VSFFAFXCjwfAAAAAHAULD4AAAAA4CgNK7tIt5xH7MoNj/Dx5mPsano7xK4mIqJEH7v+pNvX7+X3L4pxIpeScBlL525RRAyMpNilJhPFjJd41/6kOW61zxc4ssY3ydfxpXW3nRKuULlrvyqpw0UYwk3pKQgJJasnIRotcD8uC3NJ6aZOjlDKjHJWQ9+kkEti4rnwiZo/OfEZp9n2TcPivayykdnN1+lv4nsIenQZ4qDBVTTlfXjzMqpE1H2otpz1fCKSC4XOpbSX4kfY3ZsscYflY/wdS2GZ+IubRk4kOBMBASdFtFNIyA85cdLkONvMmxFvlvJBWbSLdIVrfW0nqdSoVPgV4+X7kDWQAiHup1afXi9FRpTIhG/evEyoJvpfyiiybXNLRhPPYWZUSI19IjFfi5DP/KynjBX0ys2ZFL+nKS0zkwmJ1aaG1bxQxfMia7tQXpd7PQmWLGRPhERNMU+Wv7tHPN+ahCOeYbNZJJ/rY6nkrEhc+e5NPI5XxDgi5lgfR7ikz/JvEBFR9ARf1z8itwWwPWtlG5eMNgAAAAB8WMDiAwAAAACO0riyi3TRiV25IeFSbw2xaz44rrv2z32w1GoXo2J3fkDszveLXccl4crMctuTkzvW+fyBLnaFvbdoidWWZcJPZNn95U+K66fLatLI2i52kS9uSZZENLPGxvThHN97IMl95bmg22ZclOO+u/Vtq529ms/7D+/1fP6ocDl7ROSTSNBTFEniCiHuq0KU7zXfx+7SW5cdt9rrm3nX+DuTetTU8BjbOSIkHP+4cJcK2cW1UousfSLcxp7hce201vd4yvBPcr/LqIxiRLjxxXUDE0JqC3JfXWjTk1BNMyCiHwKDbHvfBXFPMirCW/bcyb6uJnrCLWPIxq3tEZFATR5dmh1SoriamJJkhIRqEm70ghhzNgkblUwgFmd3/MTVLLeNreK39vSyPBkT0TjvJHj+IyIyRjmaJzQuZKGUiKhwiy2IbOU4o1zmmz5eVstJyaghIT17U2xDz4SQNSZYUjOzIqmhfIYHxbMwxr8jbW08Px3r5+PXxDgKpr2Z+3mwXS/Kl4/zcxEIiKxxWh/UJhIJng8AAAAAOAoWHwAAAABwlMaVXeROdrE73zPGCb2aj7A7MTyoJ1NpESXPTb8sM85ux2JEJv4SEQvFym5RU7iSh/6I3VenhLxyVuwaP5Hi417hXTNmi2KRblG31nORiOgOQ+zojojkR+HzAe0thy5wSfChdpY1/nvLm1Z7cWDcau8aW2G1x3PsWpaJjnwefl4SHXxOXGQ52tDK8sp1oTNW+3i+w2rvHrpGu1fjfbZn7CR/nneUo0Rk0q5GQO7cL08u5B3k5HmxCX5ozQiPL1OMLS3CaZLtn+9kqeXCx3mcBQ1+76E879wPCUnLSIjkR0X7MSDlI7lD33B7uXbxnWQusVSe+yZk6NKsjH5RQX7WZW2YUJMoSpURERlSnhJSS6FNROktZvuOXscyQmAV14vZ0MnJGseL/N73h3j8EBFFzrC9Q+f5C5ryWZNu/noUFqkBhpD5PGJeV/Fm7bySrPkUFBKM9psi5vwE/4bpdb0q/y7IMRBICUl5XCSeM/lz20Lcz2eC+m+ZGRBjw2PTluaQ8tgco8Xg+QAAAACAo2DxAQAAAABHaSzZxcbFo0kwaZF8Z5Jdet6ynfC+kHBByhoL0rXlkyXvZXlp8dmiDkOhU+TFN7lrR/PskjuS5zz8oxk+LmudzKjZ4nY38WwIF74xyW764JCod/C+vmt8KMZy1P+OrbfaV/Xxju17m9+z2rdHBqz2sCmSIYkaMW3eSXGc+3OZj5+dCfF8vZL6iNV+8ewfWe2zexdr99r+Hhuu6TjLAXLnvmvrVFSBKkuYpIRL2EixtOQRO/o9PptpRUSoeeOc9M8QEWYmsQ32pvutduiCiMYpiEg3KT2W9bOrElVVQsodYoiroqh7k+D+KIpaVe/nOLkdEdGNEZYMl/eft9rnru612p6iiP4bE7KNuI1iWJR872E7JoXa2HT9qNX+dC+PvSVBluT+c5DLwhdP6dFMbaf5O/mG+XmSdtWQ8+E8RItJmU57pvwsrSuReK3YIjLDEVGmm+1WDIlISZFoMSI+I5AWso387jJqJiSSu3W2WO0JYbNQO89HXUHu5ws5niO1aE0i8ubEGCpWMW9dQWI+eD4AAAAA4ChzWnxs27aN1q9fT9FolDo7O+mee+6hgYEB7RylFG3dupV6enooHA7Tpk2b6MCBAzW9aTCTY+o9+p35S9ph/pR2mi/TH8xfUVpNzDhv27ZtsI2DfJDaS79OvUy/TPyAdiRfoLcn/4vSpYR2znTuj2uvvRZ2cZBjpYP02+Kr9Frh3+j1wk9pX3EXpVVyxnkYM85ytPAu/a70Ku0o/YR2ll6qOJdNVybGmGlc5iS77Ny5kx566CFav349FYtFeuKJJ2jz5s108OBBamqacjt94xvfoKeffpq+973v0cqVK+kf/uEf6I477qCBgQGKRqOX+ITLRCZIsoks0HLvE5EhJBnNnWXY7PCVJa+Fy0uJHPuFZpGUJ8z3lC3x8fez7C5NZUQZZWGJUkRPuuUX7j29NDgzroZpqbGcYtRKihR9oN6lt9Ub9Am6k7wiguBb3/pW/W0jpSspicnkVQmWx5rf123jKbB7+GiOXe//6+N/we0Vr1rtOyODVnu5IWq7CBe+1xA7v8Xu+WNip/i/JdZZ7Rfev9FqF/dz/YNFB/R7jR4V0oP4TtPP22juDPUFPkJxbweZZNKR7F7aO/n/0YbmPyefMWXXk/mDRET0zW9+k9auXevMmKmS8nFDRTFuhLwiz5NRAHYJ54pN/N6mGEtUk8I2A0lRNycr68UImU644Wfcq8CoMJbH1DD1epZTzGgnRSa9X9pPbxVfpw2+P3V+zEgKPE4iZ7hvwmf483YMr9TecutV/Efg/7l0t9X+9h/fZrVPdrJkGBrm8eARake+hfuzsJTvY9Wyc1Z7Y8cRq93h40XB9tHrrPbh4ywvt7+nu/ZjA2KBJxLGmRdrWI2Z5+znMpqy/Sl6n4icHzOGzW+FCrP8nmvTIyvTnTwGsu0iaktM4Dkh1TTH+bn3iudeefm92VZ+PseuFVE3a/gPm79czgkaI17+XTw21m61w0O6bQJjIkJM/kbWQcKck+fjF7/4BX3xi1+k66+/ntasWUPPP/88nTx5kvbu3UtEU3/BPfPMM/TEE0/QvffeS6tXr6bvf//7NDk5SS+88ELFa+ZyOUomk9p/YO78kec26jGuomYjTlGjha4z1lOWJilJUzrs9F/Xjz76KGzjIDe2/DdaElhBzd5WinnbaXXok5RVaUqWprJBKqXoVOEQERHdfffdVdmFCLapBTf4P0U9nqsvjplWut5709SYUVN7GjBm5odq5rLT9AERYcw0Mle05yORmFpltbVNbRA8duwYDQ4O0ubNm61zgsEgbdy4kfbs2VPxGtu2baN4PG7919vbW/E8MDeKNPXnjJ+mVuRZmvoL6vbbb7fOgW2cp0hTf1n4jam/jjIqRXmV1c65lF2IYJt6YI0ZY3rMTHmyMGbml/K5LENpypPu4caYaTwuO9pFKUVbtmyhW2+9lVavntrZPDg45f7u6tJ3Ynd1ddGJEycqXufxxx+nLVu2WP9OJpP2D4XNzlotmsCmJkB5MiIlXJtGULjJZD77kkhM5BOfLXcax7id6RCyS4jfO1FgF+fJDJckz4ukQRGZSyZYVjNAyC7Sja3twpZdoBQdVvuohTqo2ZiSDPI09QPX2ckJvIhqaBsbpG0MkQxO2sNTJpU1J9nVHDrPkkfiGLsLn1h9v9X+h1XjVntVB9fO6QmzCzJdZDsdSXDyqlNDbA/fCbZTnD3LFDvO9x0Y0kvMG0kRXSWfMdGe/gtaKUUD2d9Ri7eLot6pz82behKvaWazC1FtbFMVZeNJ2+1vE3mgbGqqeCKiDHg7P/udUe7TU0V+1k8mWqy2jB+Qso6yk0mJyCApz5iV2+KeB0pvU4vRQc3G1OdO/8DVbcyUR7VN34sYD74hfoY79nMvHO7Wr/mfbWus9oNtv7baG679odV+rfdqq70/zbWtkkW+7qIAyygrwyxndvv4Pi6UOBrjx4McjfbuuxzB1P4W26jjLd3D4DnJEo6ZEuOnwvOklKLDtI9aqJ2aaUqOnZ7LynF8zIjnzRC/FTI5JRFRdpGQS65h27a08XOfN/k953I8Bop5foZ9AR5L/Yu4D/+885DVviF83GpPmGzXfxn8uNVODfCct/io/rvoP8sRS9MyGBHVpdbOZS8+Hn74YXrnnXdo9+7dM14zygaVUmrGsWmCwSAFg8GKr4HLY0C9TSlK0I3Gp2a8BtvMH4eyv6GJ0hjd1PSnlzx3NrsQwTa15pC5l1JqnNb7Pj3jNYyZ+WOA9k3NZbTpkudizDQWlyW7fOUrX6GXX36ZduzYQUuX8gq6u3tqk9G0B2SaoaGhGd4QUB8OmW/TMJ2ldcYmChn8l2aApv6qP3/+vHY+bOMM72V+Q0PFk3Rj059QyMM5AQKeSMXzYRfnOFTaS8PmGbrRd3vZmJn6ocKYmR8OqYtzGW2sOJeVA7s0FnNafCil6OGHH6YXX3yRXnvtNerv79de7+/vp+7ubtq+fbt1LJ/P086dO2nDhg21uWNQEaUUHTLfomE6TeuMjRQ29MQ+IZoavDt27LCOwTb1RylF72V+TUPFE3Rj5E8o4tF34oeNZgoY+mQKuziDUooOlfbSkHma1vlup7Ch1+QI0dQYwphxFqXUxYXHGVpHt82Yy8LUZC0Mp4FdGo85yS4PPfQQvfDCC/Szn/2MotGo5eGIx+MUDofJMAx65JFH6Mknn6QVK1bQihUr6Mknn6RIJEL333//Ja5eBVdQxGaGvir1Raljyyx7MkxQZERVId4XUgrLfR7i8uLjEll+IZUX702ztueV2x7KpU8tE2LlbHsD6m0apJO0xriFvOSn3MVNjD7yk9fwTrkjFdHTTz9NH/3oR2tvG9lXMtRWhkLKbJmyXV4gT2jeXlFErP0Ma5hte0VGwTZuD7Wwrn1GaK+GTFwrwteuEm1vVmTETYhihQlRJK4846fc5yFDvi8eP5j5NQ0WjtLayB+Tz/BT7uIeD58RIK/hI8MwqNe/ij7I76NXXnmF1qxZU1u7XCkzxpnYXCT3WskQRJnhVNhfhqgXmkSmRxFz+JsMp9JMpmSRQDGgRFZhY1Ls6yjPcCpvXWjq0/PIIXMvDZonaI33k+QjH+XUlP2nxozPcuHXbcxoexxESKV4pswLrME3H+TvsDis/4X/Q/8nrHbqZu7nr3a8brW/GOP9UIUo7xlImPysF8Q9DYiQ9x0i4++/HuGMv+ZBXkwv3s/fIf4uZyOmc/y5RPo+D22/3sX+tuYy2mA7ly1V19BROuj8mJH7imSxQpG92Z/Wn0NPge0WiPD8cYfIDvuZ+D6rvcLH802bl20ZNPQUDNMkTJ63dmd5P8d3TnOY9ZE3eT9O55tsp+YjZTmHxvnfMntwPbIFz2nx8Y//+I9ERLRp0ybt+PPPP09f/OIXiYjoa1/7GmUyGfryl79MY2NjdPPNN9Orr7467/kKFjrToWd71eva8euM9dRDV1n//tKXvgTbOMjpi2G0v5/8T+349aFbaUlgqhpvX+A6+iC/jx599FEaHx+HXRzitDmVK2Jv6TXt+PXem6jH4EUsxoyzWHMZ7dSOX0c3WnNZLy2no3QQY6aBmdPiQ9nsbpcYhkFbt26lrVu3Xu49gcvg056/uPRJNLXje9u2bXW+GzDN5tj/vOQ5039hHz58mGKx2CXOBrXiDv9fVnUexoyzfNrzF5es4WIQxkyj01iF5STVSDAyK6mv7Byb0Dszx7KL4ROZRU2bIjuVPafkT7F7bnSctWRDuJh9o9z9gaQ4PqGHnmoFl+yKlBkz3crzhp0EI5ALWZXRQ+cMv3gshTufJkSYqygg6DkqCtaJ8LCQ2Nkuwzy1/hHnGLJQlCxelpdFuOa5b51mtvBVicxkayPHSLlE1F3UpMjj2Q6+ZI7fWwoIuVHIN9r1GxktU6vovwy71M3hC1a75Te6VBlIckbR1z64yWq//FEOwV3RyxtnP9Z6puJtfJDi/v/DCQ4mCB5iCazlAx4D0eM8Dn2nWGoxpfs+rxeM01MjVJaU3YSUHAwhO2qSa0pmohXhw0TUGmBvzLjJ7X/N3GC1z63kBdS9HW9Z7av93KejJX7ufzvJ8uQvBjmz7IlD/By0CJlu2Xs8LwbOjPPNjbCsR1SWIbzOcx0KywEAAADAUbD4AAAAAICjNK7sIhHuISV2tRse4TYqc88aXnb1alk4pXxht7M5w64p/wTvXg4lROTLGf68bI5dlrKIU2SQr9l8hq/jHdPddqYo8FOeqZVfaAA5QGZnFXKMKlfEpD3kCwGx29vP9vMI2yqRoVZLOCSlNSHzGNLNKN35Nru7Z+x7krKSTRG1hqDKSDItu2hJjjsZPSaymsric00iT6kwzdgES2K7iTd6ekfYloGUKFwnd+HLsT/DNjIbawOMjwrIsaDS7NovL6AZEjJH77vs2jf/q8VqF+Oc2ff3YS4yZ4h+8hS4n1aIuc2T4OsbEyJaRcxNJZkRU953+VhqAKnFDmVTxNSQmXzL5ujYGEfsNR9lCT7/e37uj7ZxNNGTTddbbSlPykJ03hz/IzTOn71ykKVp76iI0hOStZS5Z5XE6kwDz5YAAAAAaESw+AAAAACAoywM2cUGbZeyocpeqyxfSLeydKPrO5vZ7egVrudYmt1wkbPsYi42i6gZUYDInxRSyyDvOlYJvRCTTGylGs2VbBf5Io/P5uqT8ox0NZcnJqt0Xa3wnnDbSwlOXr9k0582ETsfCspc5mpGBrwptD6VbVkUsch2bj7H7eJvWSaY8HG78wT3e9MJ4Ta2kx8cdBnXFTtZQkpMxbKCfyl2n5uif4zznODLK+Q02xghmzmlZJdkyi4KcDY5xe61KlI5uAppDzFHl8oSEZKcz09x0y/mJ381UVvSBjZbAuQ9lezkU2UjlzoMPB8AAAAAcBQsPgAAAADgKAtPdrFxG6pqPbJ2UoZ0i8kEVDaRE/5R3h0uM/Jr8o3YaWxKl3G5+7jRpBY7bKJBZiuDXdW17BKZSRnFTjqpRhZq5CiWy+FKnzHp7pUJ8mSdkgP8GZEzXJvHyPD5HpG4Sas5IcaZbfTXQsFOipitVpWY7JQsMmVU4WLXki9eQVRKo0kol0MVskbFf08fltP8XCVDmUBTystyq4HH3b8VH7JZFQAAAADzDRYfAAAAAHCUhSe72FGlK1lzhUm3moiOUdop4ridC9gu4Y5dYqdGllYuhyuVNaqQYDSqcSEvlORh84FNVIZMdGSIiDHjrJA0hbu+ZBfR9GEbH5WoVtbQzptjvzVYAjDXMEuSvqqoMuEfn26TEHEeI1mqAbMqAAAAABzFdZ6P6RTJRVW4xJlOcOm1maEqx+FraBvA7DaP1tfzMd2fM1JQzwF32caGevxVXJ7/vcZ8GG2j/U2tKns+lN0ucYc8HzW1CxXIJkWKg9TBkzFPG0uLtIDHjN3vQgMwlzHjusXHxMRUHvzd9B/zP1ir+Xz5nFzJxnuHvuvExATF4/HLfi8R0Rup/13LWwIXqYVtdpVeruUt1ZYGDUyphV12089reUuXx3zPp3WgJrZRr7i3b9x6X5egGrsY6kqWjnXANE06e/YsKaWor6+PTp06RbFYbL5vyxGSyST19vbW5TsrpWhiYoJ6enrIc5l7GGAbd9tmYGCArrvuOtilRmDMXBmNYJsP45ghqp9t5mIX13k+PB4PLV26lJLJqZS0sVjsQ/VQENXvO1/uXwjTwDbuts2SJUuICHapJRgzV46bbfNhHjNE9fne1dqlsQQlAAAAADQ8WHwAAAAAwFFcu/gIBoP09a9/nYLB4HzfimM0yndulPusJY3wnRvhHmtNo3znRrnPWtII37kR7rEeuOF7u27DKQAAAAAWNq71fAAAAABgYYLFBwAAAAAcBYsPAAAAADgKFh8AAAAAcBQsPgAAAADgKK5cfDz33HPU399PoVCI1q1bR7t27ZrvW6oZ27Zto/Xr11M0GqXOzk665557aGBgQDtHKUVbt26lnp4eCofDtGnTJjpw4MA83bEObAPbOA3s4l5gG/fietsol/GjH/1I+f1+9d3vflcdPHhQffWrX1VNTU3qxIkT831rNeHOO+9Uzz//vHr33XfVvn371Gc+8xnV19enUqmUdc5TTz2lotGo+slPfqL279+v7rvvPrV48WKVTCbn8c5hG6Vgm/kAdnEvsI17cbttXLf4uOmmm9SDDz6oHVu1apV67LHH5umO6svQ0JAiIrVz506llFKmaaru7m711FNPWedks1kVj8fVP/3TP83XbSqlYBvYxh3ALu4FtnEvbrONq2SXfD5Pe/fupc2bN2vHN2/eTHv27Jmnu6oviUSCiIja2tqIiOjYsWM0ODio9UEwGKSNGzfOax/ANrCNW4Bd3Ats417cZhtXLT5GRkaoVCpRV1eXdryrq4sGBwfn6a7qh1KKtmzZQrfeeiutXr2aiMj6nm7rA9gGtnEDsIt7gW3cixtt46v7J1wGhmFo/1ZKzTi2EHj44YfpnXfeod27d894za194Nb7qjWwjTuBXdwLbONe3GgbV3k+Ojo6yOv1zlh1DQ0NzVidNTpf+cpX6OWXX6YdO3bQ0qVLrePd3d1ERK7rA9gGtplvYBf3Atu4F7faxlWLj0AgQOvWraPt27drx7dv304bNmyYp7uqLUopevjhh+nFF1+k1157jfr7+7XX+/v7qbu7W+uDfD5PO3funNc+gG1gm/kCdnEvsI17cb1t6r6ldY5Mhz/98z//szp48KB65JFHVFNTkzp+/Ph831pN+NKXvqTi8bh6/fXX1blz56z/JicnrXOeeuopFY/H1Ysvvqj279+vPv/5z7sqNA22gW2cBHZxL7CNe3G7bVy3+FBKqW9961tq2bJlKhAIqBtuuMEKDVoIEFHF/55//nnrHNM01de//nXV3d2tgsGguu2229T+/fvn76YFsA1s4zSwi3uBbdyL221jXLxJAAAAAABHcNWeDwAAAAAsfLD4AAAAAICjYPEBAAAAAEfB4gMAAAAAjoLFBwAAAAAcBYsPAAAAADgKFh8AAAAAcBQsPgAAAADgKFh8AAAAAMBRsPgAAAAAgKNg8QEAAAAAR/n/AZEPhiuyOBgfAAAAAElFTkSuQmCC"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# TODO: you MAY add any code that you need to find `cluster_w_pca_to_digit` here.\n",
|
|
"# However, you DO NOT have to submit this code snippet. Instead, explain how\n",
|
|
"# you found your solution in words on Coursemology. Feel free to add more cells\n",
|
|
"# below, if you need to.\n",
|
|
"\n",
|
|
"f,axes = plt.subplots(2,5)\n",
|
|
"\n",
|
|
"axes[0][0].imshow(approximated_digits3[0].reshape(28, 28))\n",
|
|
"axes[0][1].imshow(approximated_digits3[1].reshape(28, 28))\n",
|
|
"axes[0][2].imshow(approximated_digits3[2].reshape(28, 28))\n",
|
|
"axes[0][3].imshow(approximated_digits3[3].reshape(28, 28))\n",
|
|
"axes[0][4].imshow(approximated_digits3[4].reshape(28, 28))\n",
|
|
"\n",
|
|
"axes[1][0].imshow(approximated_digits3[5].reshape(28, 28))\n",
|
|
"axes[1][1].imshow(approximated_digits3[6].reshape(28, 28))\n",
|
|
"axes[1][2].imshow(approximated_digits3[7].reshape(28, 28))\n",
|
|
"axes[1][3].imshow(approximated_digits3[8].reshape(28, 28))\n",
|
|
"axes[1][4].imshow(approximated_digits3[9].reshape(28, 28))\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 230,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:04:50.150335Z",
|
|
"start_time": "2024-04-13T14:04:50.143931Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"cluster_w_pca_to_digit = np.array([8, 7, 5, 2, 1, 4, 6, 9, 0, 3]) # TODO: replace the '0's with the values that you have found"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.2.6: Predicting Labels with K-Means and PCA\n",
|
|
"\n",
|
|
"Now, let us implement the function `predict_labels_kmeans_w_pca` (which we will use in the next task) to predict the *digit labels* of each digit image. \n",
|
|
"\n",
|
|
"This function accepts `centroids`, the centroids of the clusters found using K-Means clustering, `cluster_to_digit`, which maps each cluster to the digit it represents, and `digits`, which represents the images of digits whose digit labels are to be determined. In addition, it also accepts `pca` as an argument. Here, `pca` should be an instance of scikit-learn's `PCA` that is used when training the K-Means clustering model, and hence produced `centroids`.\n",
|
|
"\n",
|
|
"Then, it returns the predicted digit labels for each digit image in `digits`. Suppose the returned value is `pred_labels`, then `red_labels` should be such that `pred_labels[i]` gives the predicted digit label for the $i$-th digit image in `digits`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 244,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:13:00.296163Z",
|
|
"start_time": "2024-04-13T14:13:00.288715Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def predict_labels_kmeans_w_pca(pca, centroids, cluster_to_digit, digits):\n",
|
|
" '''\n",
|
|
" Predicts the digit labels for each digit in `digits`.\n",
|
|
" \n",
|
|
" Parameters\n",
|
|
" ----------\n",
|
|
" pca: PCA\n",
|
|
" The PCA model that is used when training the K-Means clustering model,\n",
|
|
" which produced `centroids`.\n",
|
|
" centroids: np.darray\n",
|
|
" The centroids of the clusters. Specifically, `centroids[j]` should represent\n",
|
|
" the `j`th cluster's centroid.\n",
|
|
" cluster_to_digit: np.darray\n",
|
|
" A 1D array such that `cluster_to_digit[j]` indicates which digit the `j`th\n",
|
|
" cluster represents. For example, if the 5th cluster represents the digit 0,\n",
|
|
" then `cluster_to_digit[5]` should evaluate to 0.\n",
|
|
" digits: np.darray\n",
|
|
" An `m * n` matrix, where `m` is the number of handwritten digits and `n` is\n",
|
|
" equal to 28*28. In particular, `digits[i]` represents the image\n",
|
|
" of the `i`th handwritten digit that is in the data set.\n",
|
|
" digits: np.darray\n",
|
|
" An `m * n` matrix, where `m` is the number of handwritten digits and `n` is\n",
|
|
" equal to 28*28. In particular, `digits[i]` represents the image\n",
|
|
" of the `i`th handwritten digit that is in the data set.\n",
|
|
"\n",
|
|
" Returns\n",
|
|
" -------\n",
|
|
" A 1D np.darray `pred_labels` with `m` entries such that `pred_labels[i]`\n",
|
|
" returns the predicted digit label for the image that is represented by\n",
|
|
" `digits[i]`.\n",
|
|
" '''\n",
|
|
" transformed_digits = pca.transform(digits)\n",
|
|
" result = assign_clusters(transformed_digits, centroids)\n",
|
|
" result = np.argmin(np.linalg.norm(transformed_digits[:, np.newaxis] - centroids, axis=2), axis=1)\n",
|
|
"\n",
|
|
" return cluster_to_digit[result]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 245,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:13:03.454411Z",
|
|
"start_time": "2024-04-13T14:13:03.429494Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Public test case 1\n",
|
|
"train_226_1 = np.array([[5.11821625e-01,9.50463696e-01], [1.44159613e-01,9.48649447e-01], [3.11831452e-01,4.23326449e-01], [8.27702594e-01,4.09199136e-01], [1.00549594e+02,1.00027559e+02], [1.00753513e+02,1.00538143e+02], [1.00329732e+02,1.00788429e+02], [1.00303195e+02,1.00453498e+02], [2.00134042e+02,2.00403113e+02], [2.00203455e+02,2.00262313e+02], [2.00750365e+02,2.00280409e+02], [2.00485191e+02,2.00980737e+02], [3.00961657e+02,3.00724790e+02], [3.00541227e+02,3.00276891e+02], [3.00160652e+02,3.00969925e+02], [3.00516069e+02,3.00115866e+02], [4.00623490e+02,4.00776683e+02], [4.00613003e+02,4.00917298e+02], [4.00039593e+02,4.00528589e+02], [4.00459336e+02,4.00062350e+02]])\n",
|
|
"pca_226_1 = PCA(random_state=0)\n",
|
|
"pca_226_1.fit(train_226_1)\n",
|
|
"\n",
|
|
"centroids_w_pca_226_1 = np.array([[-2.82844817e+02, 6.23948219e-01], [-1.41423461e+02, 6.36914255e-01], [-2.10522693e-03, 6.49880291e-01], [ 1.41419250e+02, 6.62846327e-01], [ 2.82840606e+02, 6.75812363e-01]])\n",
|
|
"cluster_to_digit_226_1 = np.array([2,0,4,3,1])\n",
|
|
"digits_w_pca_226_1 = np.array([[6.36961687e-01,2.69786714e-01], [4.09735239e-02,1.65276355e-02], [8.13270239e-01,9.12755577e-01], [6.06635776e-01,7.29496561e-01], [1.00543625e+02,1.00935072e+02], [1.00815854e+02,1.00002739e+02], [1.00857404e+02,1.00033586e+02], [1.00729655e+02,1.00175656e+02], [2.00863179e+02,2.00541461e+02], [2.00299712e+02,2.00422687e+02], [2.00028320e+02,2.00124283e+02], [2.00670624e+02,2.00647190e+02], [3.00615385e+02,3.00383678e+02], [3.00997210e+02,3.00980835e+02], [3.00685542e+02,3.00650459e+02], [3.00688447e+02,3.00388921e+02], [4.00135097e+02,4.00721488e+02], [4.00525354e+02,4.00310242e+02], [4.00485835e+02,4.00889488e+02], [4.00934044e+02,4.00357795e+02]])\n",
|
|
"expected_226_1 = np.array([2,2,2,2,0,0,0,0,4,4,4,4,3,3,3,3,1,1,1,1])\n",
|
|
"\n",
|
|
"assert np.all(predict_labels_kmeans_w_pca(pca_226_1, centroids_w_pca_226_1, cluster_to_digit_226_1, digits_w_pca_226_1) == expected_226_1)\n",
|
|
"\n",
|
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"# Public test case 2\n",
|
|
"train_226_2 = np.array([[5.11821625e-01,9.50463696e-01,1.44159613e-01,9.48649447e-01, 3.11831452e-01], [4.23326449e-01,8.27702594e-01,4.09199136e-01,5.49593688e-01, 2.75591132e-02], [7.53513109e-01,5.38143313e-01,3.29731716e-01,7.88428703e-01, 3.03194829e-01], [4.53497889e-01,1.34041697e-01,4.03112986e-01,2.03455241e-01, 2.62313340e-01], [1.00750365e+02,1.00280409e+02,1.00485191e+02,1.00980737e+02, 1.00961657e+02], [1.00724790e+02,1.00541227e+02,1.00276891e+02,1.00160652e+02, 1.00969925e+02], [1.00516069e+02,1.00115866e+02,1.00623490e+02,1.00776683e+02, 1.00613003e+02], [1.00917298e+02,1.00039593e+02,1.00528589e+02,1.00459336e+02, 1.00062350e+02], [2.00641328e+02,2.00852633e+02,2.00592941e+02,2.00260097e+02, 2.00839882e+02], [2.00509496e+02,2.00510889e+02,2.00753030e+02,2.00147922e+02, 2.00819627e+02], [2.00683287e+02,2.00787097e+02,2.00191616e+02,2.00802364e+02, 2.00191324e+02], [2.00081553e+02,2.00855227e+02,2.00861283e+02,2.00876537e+02, 2.00471910e+02], [3.00274048e+02,3.00007092e+02,3.00645721e+02,3.00719909e+02, 3.00835569e+02], [3.00281878e+02,3.00215218e+02,3.00639331e+02,3.00805055e+02, 3.00963671e+02], [3.00150525e+02,3.00482212e+02,3.00894716e+02,3.00422717e+02, 3.00589502e+02], [3.00024491e+02,3.00673460e+02,3.00919089e+02,3.00826825e+02, 3.00885520e+02], [4.00660355e+02,4.00245552e+02,4.00768517e+02,4.00211675e+02, 4.00831275e+02], [4.00062718e+02,4.00825488e+02,4.00164507e+02,4.00375147e+02, 4.00316738e+02], [4.00691337e+02,4.00178572e+02,4.00396256e+02,4.00005825e+02, 4.00262495e+02], [4.00421189e+02,4.00105921e+02,4.00633160e+02,4.00380424e+02, 4.00725294e+02]])\n",
|
|
"pca_226_2 = PCA(random_state=0, n_components=3)\n",
|
|
"pca_226_2.fit(train_226_2)\n",
|
|
"\n",
|
|
"centroids_w_pca_226_2 = np.array([[ 4.43887432e+02,-5.53669304e-01,-2.93314209e+00], [ 2.20280681e+02,-4.85574890e-01,-2.85614782e+00], [-3.32606971e+00,-4.17480477e-01,-2.77915355e+00], [-2.26932820e+02,-3.49386063e-01,-2.70215929e+00], [-4.50539571e+02,-2.81291650e-01,-2.62516502e+00]])\n",
|
|
"cluster_to_digit_226_2 = np.array([4,0,2,3,1])\n",
|
|
"digits_w_pca_226_2 = np.array([[6.36961687e-01,2.69786714e-01,4.09735239e-02,1.65276355e-02, 8.13270239e-01], [9.12755577e-01,6.06635776e-01,7.29496561e-01,5.43624991e-01, 9.35072424e-01], [8.15853554e-01,2.73850017e-03,8.57404277e-01,3.35855753e-02, 7.29655446e-01], [1.75655621e-01,8.63178922e-01,5.41461220e-01,2.99711891e-01, 4.22687221e-01], [1.00028320e+02,1.00124283e+02,1.00670624e+02,1.00647190e+02, 1.00615385e+02], [1.00383678e+02,1.00997210e+02,1.00980835e+02,1.00685542e+02, 1.00650459e+02], [1.00688447e+02,1.00388921e+02,1.00135097e+02,1.00721488e+02, 1.00525354e+02], [1.00310242e+02,1.00485835e+02,1.00889488e+02,1.00934044e+02, 1.00357795e+02], [2.00571530e+02,2.00321869e+02,2.00594300e+02,2.00337911e+02, 2.00391619e+02], [2.00890274e+02,2.00227158e+02,2.00623187e+02,2.00084015e+02, 2.00832644e+02], [2.00787098e+02,2.00239369e+02,2.00876484e+02,2.00058568e+02, 2.00336117e+02], [2.00150279e+02,2.00450339e+02,2.00796324e+02,2.00230642e+02, 2.00052021e+02], [3.00404552e+02,3.00198513e+02,3.00090753e+02,3.00580332e+02, 3.00298696e+02], [3.00671995e+02,3.00199515e+02,3.00942113e+02,3.00365110e+02, 3.00105495e+02], [3.00629108e+02,3.00927155e+02,3.00440377e+02,3.00954590e+02, 3.00499896e+02], [3.00425229e+02,3.00620213e+02,3.00995097e+02,3.00948944e+02, 3.00460045e+02], [4.00757729e+02,4.00497423e+02,4.00529312e+02,4.00785786e+02, 4.00414656e+02], [4.00734484e+02,4.00711143e+02,4.00932060e+02,4.00114933e+02, 4.00729015e+02], [4.00927424e+02,4.00967926e+02,4.00014706e+02,4.00863640e+02, 4.00981195e+02], [4.00957210e+02,4.00148764e+02,4.00972629e+02,4.00889936e+02, 4.00822374e+02]])\n",
|
|
"expected_226_2 = np.array([4,4,4,4,0,0,0,0,2,2,2,2,3,3,3,3,1,1,1,1])\n",
|
|
"\n",
|
|
"assert np.all(predict_labels_kmeans_w_pca(pca_226_2, centroids_w_pca_226_2, cluster_to_digit_226_2, digits_w_pca_226_2) == expected_226_2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Task 2.2.7: Comparing Approaches Used in Part 2.1 and 2.2\n",
|
|
"\n",
|
|
"Run the following two snippets of code. Then, on Coursemology,\n",
|
|
" * please specify the differences which you have observed\n",
|
|
"when/after using the different approaches discussed in part 2.1 and 2.2\n",
|
|
" * please explain what these observations suggest about our choice of 70\n",
|
|
"components for the PCA model "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 248,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:16:02.864641Z",
|
|
"start_time": "2024-04-13T14:15:55.159677Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy of K-Means (w/o PCA): 0.53\n",
|
|
"0.53\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"_, centroids = k_means(train_digits, 10, 2, 5, 2109)\n",
|
|
"pred_labels_kmeans = predict_labels_kmeans(centroids, cluster_to_digit, validation_digits)\n",
|
|
"accuracy_kmeans = compute_accuracy(pred_labels_kmeans, validation_labels)\n",
|
|
"print('Accuracy of K-Means (w/o PCA): {}'.format(accuracy_kmeans)) # might take some time to run\n",
|
|
"print(accuracy_kmeans)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 247,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-13T14:15:55.154244Z",
|
|
"start_time": "2024-04-13T14:15:43.298679Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy of K-Means (w/ PCA): 0.53\n",
|
|
"0.53\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"centroids_w_pca, pca = find_kmeans_clusters_w_pca(train_digits, 10)\n",
|
|
"pred_labels_kmeans_w_pca = predict_labels_kmeans_w_pca(pca, centroids_w_pca,\\\n",
|
|
" cluster_w_pca_to_digit, validation_digits)\n",
|
|
"accuracy_kmeans_w_pca = compute_accuracy(pred_labels_kmeans_w_pca, validation_labels)\n",
|
|
"print('Accuracy of K-Means (w/ PCA): {}'.format(accuracy_kmeans_w_pca))\n",
|
|
"print(accuracy_kmeans_w_pca)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Submission\n",
|
|
"\n",
|
|
"Once you are done, please submit your work to Coursemology, by copying the right snippets of code into the corresponding box that says \"Your answer,\" and click \"Save.\" After you save, you can make changes to your submission.\n",
|
|
"\n",
|
|
"Once you are satisfied with what you have uploaded, click \"Finalize submission.\" **Note that once your submission is finalized, it is considered to be submitted for grading and cannot be changed.** If you need to undo this action, you will have to email your assigned tutor for help. Please do not finalize your submission until you are sure that you want to submit your solutions for grading. \n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "CS2109S",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.4"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "da6084c7b4c988b21f67f6663cc709e949122dc6e2e0f1bccc66b3b1c6af1315"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|