From 705232a3d38c347c56996e57d409a9036e82e111 Mon Sep 17 00:00:00 2001 From: Eli Udler Date: Wed, 20 Nov 2019 12:24:01 -0500 Subject: [PATCH] Delete Clustering.ipynb --- Clustering.ipynb | 189 ----------------------------------------------- 1 file changed, 189 deletions(-) delete mode 100644 Clustering.ipynb diff --git a/Clustering.ipynb b/Clustering.ipynb deleted file mode 100644 index 7261033..0000000 --- a/Clustering.ipynb +++ /dev/null @@ -1,189 +0,0 @@ -{ - "cells":[ - { - "cell_type":"code", - "source":[ - "from PIL import Image\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sklearn.cluster import KMeans\n", - "from sklearn.svm import SVC\n", - "from sklearn.svm import SVR" - ], - "execution_count":0, - "outputs":[ - - ], - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":[ - "im = Image.open('2slice25.png')\n", - "pixels = list(im.getdata())\n", - "print(len(pixels))\n", - "pixels = iter(pixels)\n", - "PixelMatrix = [[] for i in range(64)]\n", - "for i in range(64):\n", - " for j in range(64):\n", - " PixelMatrix[(i + (j * 64) % 64)].append(next(pixels))\n", - "print(PixelMatrix)\n", - "\n", - "AllFeatures = []\n", - "for i in range(len(PixelMatrix)):\n", - " for j in range(len(PixelMatrix)):\n", - " Pix = [i, j]\n", - " for k in range(4):\n", - " Pix.append(PixelMatrix[i][j][k])\n", - " AllFeatures.append(Pix)\n", - "print(AllFeatures)" - ], - "execution_count":0, - "outputs":[ - { - "name":"stdout", - "text":[ - ", 239, 255], [11, 30, 138, 225, 239, 255], [11, 31, 138, 225, 239, 255], [11, 32, 140, 227, 239, 255], [11, 33, 140, 227, 239, 255], [11, 34, 140, 227, 239, 255], [11, 35, 140, 227, 239, 255], [11, 36, 111, 163, 168, 255], [11, 37, 111, 163, 168, 255], [11, 38, 90, 109, 107, 255], [11, 39, 90, 108, 106, 255], [11, 40, 72, 88, 86, 255], [11, 41, 62, 76, 73, 255], [11, 42, 56, 68, 65, 255], [11, 43, 49, 62, 60, 255], [11, 44, 43, 57, 56, 255], [11, 45, 39, 54, 52, 255], [11, 46, 41, 54, 53, 255], [11, 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46, 159, 235, 250, 255], [63, 47, 159, 235, 250, 255], [63, 48, 159, 235, 250, 255], [63, 49, 159, 235, 250, 255], [63, 50, 159, 235, 250, 255], [63, 51, 157, 235, 248, 255], [63, 52, 156, 235, 247, 255], [63, 53, 156, 235, 247, 255], [63, 54, 149, 232, 244, 255], [63, 55, 145, 228, 238, 255], [63, 56, 145, 227, 236, 255], [63, 57, 145, 227, 236, 255], [63, 58, 146, 227, 237, 255], [63, 59, 145, 230, 241, 255], [63, 60, 144, 229, 243, 255], [63, 61, 143, 228, 242, 255], [63, 62, 143, 228, 242, 255], [63, 63, 140, 225, 239, 255]]\n" - ], - "output_type":"stream" - } - ], - "metadata":{ - - } - }, - { - "cell_type":"markdown", - "source":[ - "# K-Means Clustering\n", - "We will perform clustering with k=2, using x and y position of pixels, as well as CMYK values as features\n", - "\n", - "## Applications\n", - "Knowing that a house has a pool, we can use drone images to to calculate the size of the pool by seperating the pixels in a pool image into two clusters: \n", - "part of the pool and not part of the pool\n", - "The visualization in the block below shows all pixels in the image as points, color coded based on the cluster to which they belong. \n", - "Centers of clusters are also shown." - ], - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":[ - "X=np.array(AllFeatures)\n", - "kmeans = KMeans(n_clusters=2, random_state=0).fit(X)\n", - "\n", - "labels = kmeans.labels_\n", - "centers = kmeans.cluster_centers_\n", - "print(labels)\n", - "print(centers)\n", - "\n", - "#Generate a list of lists s.t. Clusters[n] contains points [x,y] in cluster n\n", - "Clusters = []\n", - "XT=X.transpose()\n", - "for i in range(max(labels)+1):\n", - " clust = [[XT[0][j], XT[1][j]] for j in range(4096) if labels[j] == i]\n", - " Clusters.append(clust)\n", - "\n", - "#Generate a list of x values and a list of y values for one cluster\n", - "xVals0 = [i[0] for i in Clusters[0]]\n", - "yVals0 = [i[1] for i in Clusters[0]]\n", - "\n", - "#Generate a list of x values and a list of y values for the other cluster\n", - "xVals1 = [i[0] for i in Clusters[1]]\n", - "yVals1 = [i[1] for i in Clusters[1]]\n", - "\n", - "#Plot the clusters and their centers\n", - "plt.scatter(xVals1, yVals1, alpha=0.5)\n", - "plt.scatter(xVals0, yVals0, alpha=0.5)\n", - "plt.scatter(centers[0][0], centers[0][1], alpha=0.5)\n", - "plt.scatter(centers[1][0], centers[1][1], alpha=0.5)\n", - "plt.show()" - ], - "execution_count":0, - "outputs":[ - { - "name":"stdout", - "text":[ - "[0 0 0 ... 0 0 0]\n", - "[[ 35.13131027 28.50340329 148.49177538 232.80998298 245.27141237 255. ]\n", - " [ 9.03684211 50.03684211 70.20175439 85.20877193 86.3 255. ]]\n" - ], - "output_type":"stream" - }, - { - "data":{ - "image\/png":[ - 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- ] - }, - "metadata":{ - "image\/png":{ - "width":0, - "height":0 - } - }, - "output_type":"display_data" - } - ], - "metadata":{ - - } - }, - { - "cell_type":"markdown", - "source":[ - "# Support Vector Machine\n", - "Ignore this until it actually does something" - ], - "metadata":{ - - } - }, - { - "cell_type":"code", - "source":[ - "X = np.array(Clusters[0] + Clusters[1])\n", - "y = np.array([0 for i in range(len(Clusters[0]))]+[1 for i in range(len(Clusters[1]))])\n", - "\n", - "clf = SVC(gamma='auto')\n", - "clf.fit(X, y)\n", - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", - " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", - " tol=0.001, verbose=False)\n", - "print(np.array(clf.decision_function))\n", - "\n", - "clf = SVR(gamma='scale', C=1.0, epsilon=0.2)\n", - "clf.fit(X,y)\n", - "print(clf.get_params)" - ], - "execution_count":0, - "outputs":[ - { - "name":"stdout", - "text":[ - "\n", - "\n" - ], - "output_type":"stream" - } - ], - "metadata":{ - - } - } - ], - "metadata":{ - - }, - "nbformat":4, - "nbformat_minor":0 -} \ No newline at end of file