{ "cells": [ { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_digits as ld\n", "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import scale\n", "from sklearn.model_selection import train_test_split as ttSplit\n", "from sklearn.cluster import KMeans\n", "from sklearn import metrics\n", "from matplotlib import pyplot\n", "import numpy" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'data': array([[ 0., 0., 5., ..., 0., 0., 0.],\n", " [ 0., 0., 0., ..., 10., 0., 0.],\n", " [ 0., 0., 0., ..., 16., 9., 0.],\n", " ...,\n", " [ 0., 0., 1., ..., 6., 0., 0.],\n", " [ 0., 0., 2., ..., 12., 0., 0.],\n", " [ 0., 0., 10., ..., 12., 1., 0.]]), 'target': array([0, 1, 2, ..., 8, 9, 8]), 'frame': None, 'feature_names': ['pixel_0_0', 'pixel_0_1', 'pixel_0_2', 'pixel_0_3', 'pixel_0_4', 'pixel_0_5', 'pixel_0_6', 'pixel_0_7', 'pixel_1_0', 'pixel_1_1', 'pixel_1_2', 'pixel_1_3', 'pixel_1_4', 'pixel_1_5', 'pixel_1_6', 'pixel_1_7', 'pixel_2_0', 'pixel_2_1', 'pixel_2_2', 'pixel_2_3', 'pixel_2_4', 'pixel_2_5', 'pixel_2_6', 'pixel_2_7', 'pixel_3_0', 'pixel_3_1', 'pixel_3_2', 'pixel_3_3', 'pixel_3_4', 'pixel_3_5', 'pixel_3_6', 'pixel_3_7', 'pixel_4_0', 'pixel_4_1', 'pixel_4_2', 'pixel_4_3', 'pixel_4_4', 'pixel_4_5', 'pixel_4_6', 'pixel_4_7', 'pixel_5_0', 'pixel_5_1', 'pixel_5_2', 'pixel_5_3', 'pixel_5_4', 'pixel_5_5', 'pixel_5_6', 'pixel_5_7', 'pixel_6_0', 'pixel_6_1', 'pixel_6_2', 'pixel_6_3', 'pixel_6_4', 'pixel_6_5', 'pixel_6_6', 'pixel_6_7', 'pixel_7_0', 'pixel_7_1', 'pixel_7_2', 'pixel_7_3', 'pixel_7_4', 'pixel_7_5', 'pixel_7_6', 'pixel_7_7'], 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), 'images': array([[[ 0., 0., 5., ..., 1., 0., 0.],\n", " [ 0., 0., 13., ..., 15., 5., 0.],\n", " [ 0., 3., 15., ..., 11., 8., 0.],\n", " ...,\n", " [ 0., 4., 11., ..., 12., 7., 0.],\n", " [ 0., 2., 14., ..., 12., 0., 0.],\n", " [ 0., 0., 6., ..., 0., 0., 0.]],\n", "\n", " [[ 0., 0., 0., ..., 5., 0., 0.],\n", " [ 0., 0., 0., ..., 9., 0., 0.],\n", " [ 0., 0., 3., ..., 6., 0., 0.],\n", " ...,\n", " [ 0., 0., 1., ..., 6., 0., 0.],\n", " [ 0., 0., 1., ..., 6., 0., 0.],\n", " [ 0., 0., 0., ..., 10., 0., 0.]],\n", "\n", " [[ 0., 0., 0., ..., 12., 0., 0.],\n", " [ 0., 0., 3., ..., 14., 0., 0.],\n", " [ 0., 0., 8., ..., 16., 0., 0.],\n", " ...,\n", " [ 0., 9., 16., ..., 0., 0., 0.],\n", " [ 0., 3., 13., ..., 11., 5., 0.],\n", " [ 0., 0., 0., ..., 16., 9., 0.]],\n", "\n", " ...,\n", "\n", " [[ 0., 0., 1., ..., 1., 0., 0.],\n", " [ 0., 0., 13., ..., 2., 1., 0.],\n", " [ 0., 0., 16., ..., 16., 5., 0.],\n", " ...,\n", " [ 0., 0., 16., ..., 15., 0., 0.],\n", " [ 0., 0., 15., ..., 16., 0., 0.],\n", " [ 0., 0., 2., ..., 6., 0., 0.]],\n", "\n", " [[ 0., 0., 2., ..., 0., 0., 0.],\n", " [ 0., 0., 14., ..., 15., 1., 0.],\n", " [ 0., 4., 16., ..., 16., 7., 0.],\n", " ...,\n", " [ 0., 0., 0., ..., 16., 2., 0.],\n", " [ 0., 0., 4., ..., 16., 2., 0.],\n", " [ 0., 0., 5., ..., 12., 0., 0.]],\n", "\n", " [[ 0., 0., 10., ..., 1., 0., 0.],\n", " [ 0., 2., 16., ..., 1., 0., 0.],\n", " [ 0., 0., 15., ..., 15., 0., 0.],\n", " ...,\n", " [ 0., 4., 16., ..., 16., 6., 0.],\n", " [ 0., 8., 16., ..., 16., 8., 0.],\n", " [ 0., 1., 8., ..., 12., 1., 0.]]]), 'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 1797\\n:Number of Attributes: 64\\n:Attribute Information: 8x8 image of integer pixels in the range 0..16.\\n:Missing Attribute Values: None\\n:Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\\n:Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n Graduate Studies in Science and Engineering, Bogazici University.\\n- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n Linear dimensionalityreduction using relevance weighted LDA. School of\\n Electrical and Electronic Engineering Nanyang Technological University.\\n 2005.\\n- Claudio Gentile. A New Approximate Maximal Margin Classification\\n Algorithm. NIPS. 2000.\\n\\n|details-end|\\n\"}\n" ] } ], "source": [ "DataSet = ld()\n", "print(DataSet)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1797, 64)" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DataSet.data.shape" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "pcA = PCA(2)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1797, 2)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pcAData = pcA.fit_transform(DataSet.data)\n", "pcAData.shape\n", "#pyplot.plot(pcAData)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "28.509364823692117" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(pcA.explained_variance_ratio_*100)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "ClusterDataSet = ld()\n", "ClusterDataSet.data = scale(ClusterDataSet.data)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "Kmean = KMeans(init='k-means++',n_clusters=10,random_state=0)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "data_train = pcA.fit_transform(ClusterDataSet.data)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "TrainSet_train,TrainSet_test,TestSet_train,TestSet_test = ttSplit(data_train,ClusterDataSet.target,test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([7, 0, 5, 3, 5, 5, 5, 6, 0, 2, 4, 4, 4, 9, 7, 7, 0, 5, 5, 0, 0, 0,\n", " 6, 8, 6, 5, 6, 5, 8, 5, 1, 3, 2, 6, 0, 8, 9, 2, 1, 7, 5, 7, 7, 8,\n", " 8, 2, 1, 1, 5, 8, 6, 1, 7, 6, 8, 8, 6, 2, 0, 8, 9, 0, 6, 6, 9, 6,\n", " 5, 4, 5, 6, 5, 6, 4, 6, 7, 5, 1, 4, 5, 8, 8, 8, 5, 7, 6, 7, 1, 9,\n", " 0, 5, 2, 5, 0, 5, 9, 5, 9, 9, 3, 4, 5, 8, 1, 6, 3, 0, 7, 3, 7, 7,\n", " 2, 2, 9, 5, 1, 9, 5, 5, 9, 3, 7, 0, 7, 5, 4, 8, 8, 1, 6, 7, 1, 6,\n", " 8, 7, 0, 2, 7, 2, 8, 3, 8, 7, 4, 5, 3, 2, 3, 3, 6, 8, 2, 0, 1, 2,\n", " 3, 1, 0, 7, 8, 5, 4, 0, 6, 0, 6, 7, 1, 6, 6, 7, 6, 0, 1, 3, 5, 6,\n", " 2, 5, 2, 8, 3, 2, 9, 6, 5, 6, 2, 9, 9, 0, 2, 2, 2, 5, 0, 8, 5, 7,\n", " 0, 6, 5, 0, 9, 9, 1, 8, 2, 2, 7, 1, 9, 9, 1, 5, 3, 0, 5, 5, 8, 5,\n", " 6, 0, 8, 4, 9, 8, 1, 5, 8, 5, 0, 5, 9, 4, 1, 6, 8, 1, 9, 7, 8, 4,\n", " 5, 5, 9, 9, 6, 6, 1, 5, 0, 7, 5, 3, 1, 8, 7, 6, 6, 1, 3, 7, 1, 2,\n", " 3, 2, 3, 1, 5, 5, 5, 3, 8, 5, 9, 1, 7, 6, 8, 8, 0, 1, 1, 9, 9, 8,\n", " 6, 7, 6, 6, 1, 6, 8, 0, 1, 2, 6, 7, 8, 6, 2, 0, 7, 7, 2, 3, 0, 7,\n", " 8, 3, 3, 8, 5, 4, 9, 8, 1, 8, 5, 2, 3, 8, 1, 7, 6, 1, 0, 0, 5, 5,\n", " 9, 5, 5, 8, 4, 7, 8, 6, 5, 9, 5, 5, 9, 9, 4, 1, 9, 7, 5, 3, 3, 9,\n", " 1, 8, 7, 5, 0, 5, 7, 6])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Kmean.fit(TrainSet_train)\n", "Label_K = Kmean.predict(TrainSet_test) \n", "cm = metrics.confusion_matrix(TestSet_test, Label_K)\n", "metrics.ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n", "Label_K" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "Cluster_center = Kmean.cluster_centers_ " ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure(figsize=(10, 10)) \n", "for i in numpy.unique(Label_K): \n", " pyplot.scatter(TrainSet_test[Label_K == i, 0], TrainSet_test[Label_K == i, 1], label=i) \n", "pyplot.scatter(Cluster_center[:, 0], Cluster_center[:, 1], marker='x', s=100, linewidths=2, color='k', zorder=10) \n", "pyplot.legend() \n", "pyplot.show()" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }