├── .gitattributes ├── EE236A_Project_part1.pdf ├── EE236A_Project_part2.pdf ├── MyClassifier ├── MyClassifier_19_Part1.py ├── MyClassifier_19_Part2.py ├── mnist_test.csv └── mnist_train.csv ├── README.md ├── Report_19.pdf └── csv ├── bias_19_Part1_Gaussian.csv ├── bias_19_Part1_MNIST.csv ├── bias_19_Part2_Gaussian.csv ├── bias_19_Part2_MNIST.csv ├── weights_19_Part1_Gaussian.csv ├── weights_19_Part1_MNIST.csv ├── weights_19_Part2_Gaussian.csv └── weights_19_Part2_MNIST.csv /.gitattributes: -------------------------------------------------------------------------------- 1 | mnist_train.csv filter=lfs diff=lfs merge=lfs -text 2 | -------------------------------------------------------------------------------- /EE236A_Project_part1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/weftuon1/Linear-Programming/c8894bddd8539035cb1bd98bf23ba678cff62cfe/EE236A_Project_part1.pdf -------------------------------------------------------------------------------- /EE236A_Project_part2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/weftuon1/Linear-Programming/c8894bddd8539035cb1bd98bf23ba678cff62cfe/EE236A_Project_part2.pdf -------------------------------------------------------------------------------- /MyClassifier/MyClassifier_19_Part1.py: -------------------------------------------------------------------------------- 1 | # For data preprocess 2 | import numpy as np 3 | import csv 4 | import os 5 | from math import exp, log 6 | # For plotting 7 | import matplotlib.pyplot as plt 8 | from matplotlib.pyplot import figure 9 | 10 | # Fixed random seed 11 | np.random.seed(0) 12 | 13 | # Load dataset1 Gaussian 14 | def dataset_gaussian(n): 15 | cov = [[1, 0], [0, 1]] 16 | data1 = np.random.multivariate_normal([-1, 1], cov, n) 17 | data2 = np.random.multivariate_normal([1, -1], cov, n) 18 | c1 = np.array([[1] * n]) 19 | data1 = np.concatenate((c1.T, data1), axis=1) # Set data label as the first element in array 20 | c2 = np.array([[0] * n]) 21 | data2 = np.concatenate((c2.T, data2), axis=1) # Set data label as the first element in array 22 | data = np.concatenate((data1, data2), axis=0) 23 | np.random.shuffle(data) 24 | return data 25 | 26 | # Load dataset2 MNIST 27 | def dataset_MNIST(path, chr): 28 | datadim = 10000 if path == 'mnist_test.csv' else 60000 29 | c1, c2 = chr 30 | with open(path, 'r') as fp: 31 | data = list(csv.reader(fp)) 32 | idx = [i for i in range(1, datadim+1) if data[i][0] == c1 or data[i][0] == c2] # Choose the characters we want to classify, ex: '1' and '7' 33 | data = np.array(data)[idx, :].astype(float) 34 | for i in range(len(data)): 35 | data[i][0] = 1 if data[i][0] == int(c1) else 0 # Convert character c1 into 1 or c2 into 0 36 | return data 37 | 38 | # sigmoid 39 | def sigmoid(x): 40 | if x <= -709: 41 | return 0 42 | return 1/(1+exp(-x)) 43 | 44 | # 2-norm 45 | def norm(x): 46 | return sum([i ** 2 for i in x]) ** 0.5 47 | 48 | class MyClassifier: 49 | def __init__(self): 50 | self.theta = None # weight coefficient 51 | self.dim = None # 1 + the number of features 52 | self.x = None # The features of current chosen data 53 | self.y = None # The labels of current chosen data 54 | self.m = None # total # of samples 55 | self.n = None # current # of samples 56 | self.mean = None # current mean of all chosen samples 57 | self.std = None # current standard deviation of all chosen samples 58 | def sample_selection(self, training_sample): # Data label is in the first element of training_sample 59 | self.m, self.dim = np.shape(training_sample) 60 | self.theta = np.zeros(self.dim) 61 | self.n = self.m // config['pre_train'] # parameter 62 | self.x = training_sample[:self.n, 1:] 63 | self.y = training_sample[:self.n, 0] 64 | training_data = self.normalize() 65 | self.train(training_data, self.y) 66 | # select samples that matter 67 | cnt = 0 68 | for i in range(self.n, self.m): 69 | x = training_sample[i, 1:] - self.mean 70 | for j in range(len(x)): 71 | if self.std[j] != 0: 72 | x[j] /= self.std[j] 73 | x = np.concatenate((np.array([1]).T, x), axis=0) 74 | y = training_sample[i, 0] 75 | b = np.inner(self.theta, x) 76 | if abs(b) / norm(self.theta) >= config['boundary']: # if the distance between the data point and the current classifier is larger than this 'boundary', we don't choose this data point 77 | continue 78 | self.x = np.concatenate((self.x, [training_sample[i, 1:]]), axis=0) 79 | self.y = np.concatenate((self.y, [y]), axis=0) 80 | cnt += 1 81 | self.n += 1 82 | if cnt == self.m//config['batch']: # parameter 83 | training_data = self.normalize() 84 | self.train(training_data, self.y) 85 | cnt = 0 86 | return 87 | def train(self, train_data, train_label): 88 | for _ in range(config['iterate']): # parameter 89 | self.gd(train_data, train_label, self.n, self.theta) 90 | return 91 | def f(self, input): 92 | t = np.inner(self.theta, np.concatenate((np.array([1]).T,input),axis=0)) 93 | return 1 if t > 0 else 0 94 | def test(self, input): 95 | input_size = len(input) 96 | ans = 0 97 | input[:, 1:] -= self.mean 98 | for i in range(1, len(input[0])): 99 | if self.std[i-1] != 0: 100 | input[:, i] /= self.std[i-1] 101 | for i in range(input_size): 102 | if self.f(input[i][1:]) == input[i][0]: 103 | ans += 1 104 | return ans/input_size 105 | def calculate_lost(self, x, y, n, theta): 106 | ans = 0 107 | for i in range(n): 108 | t = np.inner(theta, x[i]) 109 | ans += (y[i] * log(sigmoid(t))) + (1-y[i]) * log(1-sigmoid(t)) 110 | return -ans/n 111 | def gd(self, x, y, n, theta): 112 | t = np.array([sigmoid(np.inner(theta, x[i]))-y[i] for i in range(n)]) 113 | theta -= config['lr'] * np.matmul(x.T, t) 114 | def normalize(self): 115 | self.mean = np.mean(self.x, axis=0) 116 | self.std = np.std(self.x, axis=0) 117 | c = np.array([[1]*self.n]) # constant 118 | training_data = self.x - self.mean 119 | for i in range(len(training_data[0])): 120 | if self.std[i] != 0: 121 | training_data[:, i] /= self.std[i] 122 | training_data = np.concatenate((c.T, training_data), axis=1) 123 | return training_data 124 | 125 | # Change the parameters here 126 | config = { 127 | 'pre_train':100, # We pretrain 1/100 of total samples 128 | 'batch':1000, # We train after seeing 1/1000 of total samples closed to the boundary 129 | 'boundary':0.5, # We decide to choose the data point which is less than this 'boundary' away from the hyperplane 130 | 'iterate':50, # times of iteration 131 | 'lr':0.001, # learning rate 132 | 'classify':('1','7') # classify which two characters 133 | } 134 | 135 | # Load dataset 136 | # Uncomment here to load dataset1 Gaussian and please comment dataset2 MNIST 137 | data = dataset_gaussian(6000) 138 | testdata = dataset_gaussian(1000) 139 | # Uncomment here to load dataset2 MNIST and please comment dataset1 Gaussian 140 | ''' 141 | data = dataset_MNIST('mnist_train.csv', config['classify']) 142 | testdata = dataset_MNIST('mnist_test.csv', config['classify']) 143 | ''' 144 | 145 | # Create Class MyClassifier 146 | a = MyClassifier() 147 | a.sample_selection(data) # Main function to run our program 148 | 149 | # Print the results 150 | print('Total Samples: {}'.format(a.m)) 151 | print('Total Chosen Samples: {}'.format(a.n)) 152 | print('Accuracy: {}'.format(a.test(testdata))) # Test 153 | 154 | # Plot chosen sample points for Gaussian dataset 155 | ''' 156 | idx1 = [i for i in range(a.n) if a.y[i] == 1] 157 | idx2 = [i for i in range(a.n) if a.y[i] == 0] 158 | plt.scatter(a.x[idx1, 0], a.x[idx1, 1]) 159 | plt.scatter(a.x[idx2, 0], a.x[idx2, 1]) 160 | plt.show() 161 | ''' 162 | 163 | # ouput csv files 164 | ''' 165 | pathb, pathw = 'bias_19_Part1_', 'weights_19_Part1_' 166 | pathb += 'Gaussian.csv' if len(a.theta) == 3 else 'MNIST.csv' 167 | pathw += 'Gaussian.csv' if len(a.theta) == 3 else 'MNIST.csv' 168 | with open(pathb, 'w') as fp: 169 | writer = csv.writer(fp) 170 | writer.writerow(['bias']) 171 | writer.writerow([a.theta[0]]) 172 | with open(pathw, 'w') as fp: 173 | writer = csv.writer(fp) 174 | writer.writerow(['weights']) 175 | for p in a.theta[1:]: 176 | writer.writerow([p]) 177 | ''' 178 | -------------------------------------------------------------------------------- /MyClassifier/MyClassifier_19_Part2.py: -------------------------------------------------------------------------------- 1 | # For data preprocess 2 | import numpy as np 3 | import csv 4 | import os 5 | from math import exp, log 6 | # For plotting 7 | import matplotlib.pyplot as plt 8 | from matplotlib.pyplot import figure 9 | 10 | # Fixed random seed 11 | np.random.seed(0) 12 | 13 | # Load dataset1 Gaussian 14 | def dataset_gaussian(n): 15 | cov = [[1, 0], [0, 1]] 16 | data1 = np.random.multivariate_normal([-1, 1], cov, n) 17 | data2 = np.random.multivariate_normal([1, -1], cov, n) 18 | c1 = np.array([[1] * n]) 19 | data1 = np.concatenate((c1.T, data1), axis=1) # Set data label as the first element in array 20 | c2 = np.array([[0] * n]) 21 | data2 = np.concatenate((c2.T, data2), axis=1) # Set data label as the first element in array 22 | data = np.concatenate((data1, data2), axis=0) 23 | np.random.shuffle(data) 24 | return data 25 | 26 | # Load dataset2 MNIST 27 | def dataset_MNIST(path, chr): 28 | datadim = 10000 if path == 'mnist_test.csv' else 60000 29 | c1, c2 = chr 30 | with open(path, 'r') as fp: 31 | data = list(csv.reader(fp)) 32 | idx = [i for i in range(1, datadim+1) if data[i][0] == c1 or data[i][0] == c2] # Choose the characters we want to classify, ex: '1' and '7' 33 | data = np.array(data)[idx, :].astype(float) 34 | for i in range(len(data)): 35 | data[i][0] = 1 if data[i][0] == int(c1) else 0 # Convert character c1 into 1 or c2 into 0 36 | return data 37 | 38 | # sigmoid 39 | def sigmoid(x): 40 | if x <= -709: 41 | return 0 42 | return 1/(1+exp(-x)) 43 | 44 | # 2-norm 45 | def norm(x): 46 | return sum([i ** 2 for i in x]) ** 0.5 47 | 48 | class MyClassifier: 49 | def __init__(self): 50 | self.theta = None # weight coefficient 51 | self.dim = None # 1 + the number of features 52 | self.x = None # The features of current chosen data 53 | self.y = None # The labels of current chosen data 54 | self.m = None # total # of samples 55 | self.n = None # current # of samples 56 | self.mean = None # current mean of all chosen samples 57 | self.std = None # current standard deviation of all chosen samples 58 | def sample_selection(self, training_sample): # Data label is in the first element of training_sample 59 | self.m, self.dim = np.shape(training_sample) 60 | nor_data = self.normalize(training_sample[:, 1:]) 61 | def label_center(): 62 | idx0, idx1 = [], [] 63 | for i in range(self.m): 64 | if training_sample[i][0]: 65 | idx1.append(i) 66 | else: 67 | idx0.append(i) 68 | ret = [] 69 | ret.append(np.mean(nor_data[idx0, :], axis=0)) 70 | ret.append(np.mean(nor_data[idx1, :], axis=0)) 71 | return ret 72 | p0, p1 = label_center() # find two center points of two labels 73 | v = p1 - p0 74 | dis = norm(v) 75 | c = -np.inner(v, (p0+p1)/2) 76 | idx = [] 77 | for i in range(self.m): 78 | b = np.inner(v, nor_data[i, :])+c 79 | l = training_sample[i, 0] 80 | # Choose Method here 81 | # Uncomment here to use Method1 'Perpendicular-Bisector' and please comment Method2 'Distance-Ratio' 82 | # if ((b > 0 and l == 1) or (b <= 0 and l == 0)) and abs(b/(dis ** 2)) <= config['dis']: 83 | # Uncomment here to use Method2 'Distance-Ratio' and please comment Method1 'Perpendicular-Bisector' 84 | if ((b > 0 and l == 1) or (b <= 0 and l == 0)) and 1/config['divide'] <= norm(nor_data[i, :]-p0)/norm(nor_data[i, :]-p1) <= config['divide']: 85 | idx.append(i) 86 | self.theta = np.zeros(self.dim) 87 | self.n = len(idx) 88 | self.x = training_sample[idx, 1:] 89 | self.y = training_sample[idx, 0] 90 | c = np.array([[1]*self.n]) # constant 91 | training_data = self.normalize(self.x) 92 | training_data = np.concatenate((c.T, training_data), axis=1) 93 | self.train(training_data, self.y) 94 | return 95 | def train(self, train_data, train_label): 96 | for _ in range(config['iterate']): # parameter 97 | self.gd(train_data, train_label, self.n, self.theta) 98 | return 99 | def f(self, input): 100 | t = np.inner(self.theta, np.concatenate((np.array([1]).T,input),axis=0)) 101 | return 1 if t > 0 else 0 102 | def test(self, input): 103 | input_size = len(input) 104 | ans = 0 105 | input[:, 1:] -= self.mean 106 | for i in range(1, len(input[0])): 107 | if self.std[i-1] != 0: 108 | input[:, i] /= self.std[i-1] 109 | for i in range(input_size): 110 | if self.f(input[i][1:]) == input[i][0]: 111 | ans += 1 112 | return ans/input_size 113 | def calculate_lost(self, x, y, n, theta): 114 | ans = 0 115 | for i in range(n): 116 | t = np.inner(theta, x[i]) 117 | ans += (y[i] * log(sigmoid(t))) + (1-y[i]) * log(1-sigmoid(t)) 118 | return -ans/n 119 | def gd(self, x, y, n, theta): 120 | t = np.array([sigmoid(np.inner(theta, x[i]))-y[i] for i in range(n)]) 121 | theta -= config['lr'] * np.matmul(x.T, t) 122 | def normalize(self, vector): 123 | self.mean = np.mean(vector, axis=0) 124 | self.std = np.std(vector, axis=0) 125 | nor_data = vector - self.mean 126 | for i in range(len(nor_data[0])): 127 | if self.std[i] != 0: 128 | nor_data[:, i] /= self.std[i] 129 | return nor_data 130 | 131 | # Change the parameters here 132 | config = { 133 | 'dis':0.25, # We decide to choose the data point whose ratio of the distance to the perpendicular and the distance of two center points is smaller than this 'dis' and in the right halfspace 134 | 'divide':1.15, # We decide to choose the data point whose ratio of two distances to two center points is smaller than this 'divide' and larger than '1/divide' 135 | 'iterate':100, # times of iteration 136 | 'lr':0.001, # learning rate 137 | 'classify':('1','7') # classify which two characters 138 | } 139 | 140 | # Load dataset 141 | # Uncomment here to load dataset1 Gaussian and please comment dataset2 MNIST 142 | data = dataset_gaussian(6000) 143 | testdata = dataset_gaussian(1000) 144 | # Uncomment here to load dataset2 MNIST and please comment dataset1 Gaussian 145 | ''' 146 | data = dataset_MNIST('mnist_train.csv', config['classify']) 147 | testdata = dataset_MNIST('mnist_test.csv', config['classify']) 148 | ''' 149 | 150 | # Create Class MyClassifier 151 | a = MyClassifier() 152 | a.sample_selection(data) # Main function to run our program 153 | 154 | # Print the results 155 | print('Total Samples: {}'.format(a.m)) 156 | print('Total Chosen Samples: {}'.format(a.n)) 157 | print('Accuracy: {}'.format(a.test(testdata))) # Test 158 | 159 | # Plot chosen sample points for Gaussian dataset 160 | ''' 161 | idx1 = [i for i in range(a.n) if a.y[i] == 1] 162 | idx2 = [i for i in range(a.n) if a.y[i] == 0] 163 | plt.scatter(a.x[idx1, 0], a.x[idx1, 1]) 164 | plt.scatter(a.x[idx2, 0], a.x[idx2, 1]) 165 | plt.show() 166 | ''' 167 | 168 | # ouput csv files 169 | ''' 170 | pathb, pathw = 'bias_19_Part2_', 'weights_19_Part2_' 171 | pathb += 'Gaussian.csv' if len(a.theta) == 3 else 'MNIST.csv' 172 | pathw += 'Gaussian.csv' if len(a.theta) == 3 else 'MNIST.csv' 173 | with open(pathb, 'w') as fp: 174 | writer = csv.writer(fp) 175 | writer.writerow(['bias']) 176 | writer.writerow([a.theta[0]]) 177 | with open(pathw, 'w') as fp: 178 | writer = csv.writer(fp) 179 | writer.writerow(['weights']) 180 | for p in a.theta[1:]: 181 | writer.writerow([p]) 182 | ''' 183 | -------------------------------------------------------------------------------- /MyClassifier/mnist_train.csv: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:06e64b0bf65de89c522348319ce5f63f43fb1cb40e1ce3c2ff43c1d3b8a0e14c 3 | size 109640201 4 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Linear Programming 2 | Linear Programming Project for ECE 236A (Linear Programming) at UCLA, Fall 2021 3 | 4 | - Project Part I - Compression of Data for Learning 5 | - Project Part II - Bounds on Data Compression 6 | -------------------------------------------------------------------------------- /Report_19.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/weftuon1/Linear-Programming/c8894bddd8539035cb1bd98bf23ba678cff62cfe/Report_19.pdf -------------------------------------------------------------------------------- /csv/bias_19_Part1_Gaussian.csv: -------------------------------------------------------------------------------- 1 | bias 2 | 0.014774062810703092 3 | -------------------------------------------------------------------------------- /csv/bias_19_Part1_MNIST.csv: -------------------------------------------------------------------------------- 1 | bias 2 | -0.2811047027500083 3 | -------------------------------------------------------------------------------- /csv/bias_19_Part2_Gaussian.csv: -------------------------------------------------------------------------------- 1 | bias 2 | 0.08180168397604888 3 | -------------------------------------------------------------------------------- /csv/bias_19_Part2_MNIST.csv: -------------------------------------------------------------------------------- 1 | bias 2 | -0.31143885290485723 3 | -------------------------------------------------------------------------------- /csv/weights_19_Part1_Gaussian.csv: -------------------------------------------------------------------------------- 1 | weights 2 | -1.6402690515944045 3 | 1.6993063734925447 4 | -------------------------------------------------------------------------------- /csv/weights_19_Part1_MNIST.csv: 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