hw2/hw2_12.py

@@ -14,7 +14,7 @@ def decision_stump(x, y):
     
     best_Ein = 1e9
     theta_ans =  np.random.uniform(-1, 1, 1)[0]
-    sign_ans = 1 if ( np.random.uniform(-1, 1, 1)[0] > 0 ) else -1
+    sign_ans = ( np.random.uniform(-1, 1, 1)[0] > 0 )
 
     h_of_x = sign_ans * np.sign(x-np.array([theta_ans]*x.shape[0]))
     Ein = (h_of_x != y).sum()

hw3/hw3_10.py

@@ -1,51 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-
-
-def generate_data(N):
-    y = np.random.choice([1, -1], N)
-
-
-    x = np.empty((N, 3))
-    for index, i in enumerate(y):
-        if i == 1:
-            mean = [3, 2]
-            covariance = [[0.4, 0], [0, 0.4]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-        else:
-            mean = [5, 0]
-            covariance = [[0.6, 0], [0, 0.6]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-    return x, y
-
-def average_square_error(y, y_hat):
-    error = (y==y_hat)
-    return error.sum()/error.shape[0]
-
-if __name__ == '__main__':
-    errors = []
-    for times in range(128):
-        np.random.seed(times)
-        train_x, train_y = generate_data(256)               # (256, 3), (256, )
-        test_x, test_y = generate_data(4096)
-
-        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
-        w = pseudo_inverse_x @ train_y                      # (3)
-        
-        predict_y = train_x @ w
-        predict_y = np.sign(predict_y)
-
-        error = average_square_error(predict_y, train_y)
-        errors.append(error)
-        print(times, error)
-    
-    errors = sorted(errors)
-    median = ( errors[63] + errors[64] ) / 2 
-
-    plt.hist(errors, bins=10)
-    plt.xlabel("Ein")
-    plt.title("median: {}".format(median))
-    plt.savefig("10.png")

hw3/hw3_11.py

@@ -1,89 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-
-
-def generate_data(N):
-    y = np.random.choice([1, -1], N)
-
-
-    x = np.empty((N, 3))
-    for index, i in enumerate(y):
-        if i == 1:
-            mean = [3, 2]
-            covariance = [[0.4, 0], [0, 0.4]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-        else:
-            mean = [5, 0]
-            covariance = [[0.6, 0], [0, 0.6]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-    return x, y
-
-def average_square_error(y, y_hat):
-    error = (y!=y_hat)
-    return error.sum()/error.shape[0]
-
-def sigmoid(s):
-    return 1/(1+np.exp(-s))
-
-def gradient(w, x, y):
-    y = y.reshape((-1, 1))
-    theta = sigmoid( - x@w * y )
-    yx = -y * x
-    result = theta * yx
-
-    grad = np.mean(result, axis=0)
-    return grad.reshape((3, 1))
-
-if __name__ == '__main__':
-    linear_regression_errors = []
-    logistic_regression_errors = []
-    
-    for times in range(128):
-
-        # generate data
-        np.random.seed(times)
-        train_x, train_y = generate_data(256)               # (256, 3), (256, )
-        test_x, test_y = generate_data(4096)
-
-        # linear regression
-        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
-        w = pseudo_inverse_x @ train_y                      # (3)
-        
-        predict_y = test_x @ w
-        predict_y = np.sign(predict_y)
-
-        error = average_square_error(predict_y, test_y)
-        linear_regression_errors.append(error)
-        print(times, error)
-
-        # logistic regression
-        lr = 0.1
-        T = 500
-        w0 = np.zeros((3, 1))
-        for iter in range(T):
-            grad =  gradient(w0, train_x, train_y)
-            w0 -= lr * grad
-        w0 = w0.reshape((3))
-
-        predict_y = test_x @ w0
-        predict_y = np.sign(predict_y)
-
-        error = average_square_error(predict_y, test_y)
-        logistic_regression_errors.append(error)
-        print(times, error)
-        print()
-    
-
-    linear_regression_errors = sorted(linear_regression_errors)
-    logistic_regression_errors = sorted(logistic_regression_errors)
-    linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64]
-    logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64]
-
-    plt.scatter(linear_regression_errors, logistic_regression_errors)
-    plt.xlabel("linear regression error")
-    plt.xlabel("logistic regression error")
-    plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median))
-    plt.savefig("11.png")

hw3/hw3_12.py

@@ -1,103 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-
-
-def generate_data(N):
-    y = np.random.choice([1, -1], N)
-
-    x = np.empty((N, 3))
-    for index, i in enumerate(y):
-        if i == 1:
-            mean = [3, 2]
-            covariance = [[0.4, 0], [0, 0.4]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-        else:
-            mean = [5, 0]
-            covariance = [[0.6, 0], [0, 0.6]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-    return x, y
-
-def generate_outliers(N):
-    y = np.ones((N))
-
-    x = np.empty((N, 3))
-    for index, i in enumerate(y):
-        mean = [0, 6]
-        covariance = [[0.1, 0], [0, 0.3]]
-        x1, x2 = np.random.multivariate_normal(mean, covariance)
-        x[index] = np.array([1, x1, x2])
-    return x, y
-
-def average_square_error(y, y_hat):
-    error = (y!=y_hat)
-    return error.sum()/error.shape[0]
-
-def sigmoid(s):
-    return 1/(1+np.exp(-s))
-
-def gradient(w, x, y):
-    y = y.reshape((-1, 1))
-    theta = sigmoid( - x@w * y )
-    yx = -y * x
-    result = theta * yx
-
-    grad = np.mean(result, axis=0)
-    return grad.reshape((3, 1))
-
-if __name__ == '__main__':
-    linear_regression_errors = []
-    logistic_regression_errors = []
-    
-    for times in range(128):
-
-        # generate data
-        np.random.seed(times)
-        train_x, train_y = generate_data(256)               # (256, 3), (256, )
-        test_x, test_y = generate_data(4096)
-        outlier_x, outlier_y = generate_outliers(16)
-
-        train_x = np.concatenate((train_x, outlier_x), axis=0)
-        train_y = np.concatenate((train_y, outlier_y), axis=0)
-
-        # linear regression
-        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
-        w = pseudo_inverse_x @ train_y                      # (3)
-        
-        predict_y = test_x @ w
-        predict_y = np.sign(predict_y)
-
-        error = average_square_error(predict_y, test_y)
-        linear_regression_errors.append(error)
-        print(times, error)
-
-        # logistic regression
-        lr = 0.1
-        T = 500
-        w0 = np.zeros((3, 1))
-        for iter in range(T):
-            grad =  gradient(w0, train_x, train_y)
-            w0 -= lr * grad
-        w0 = w0.reshape((3))
-
-        predict_y = test_x @ w0
-        predict_y = np.sign(predict_y)
-
-        error = average_square_error(predict_y, test_y)
-        logistic_regression_errors.append(error)
-        print(times, error)
-        print()
-    
-
-    linear_regression_errors = sorted(linear_regression_errors)
-    logistic_regression_errors = sorted(logistic_regression_errors)
-    linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64]
-    logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64]
-
-    plt.scatter(linear_regression_errors, logistic_regression_errors)
-    plt.xlabel("linear regression error")
-    plt.xlabel("logistic regression error")
-    plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median))
-    plt.savefig("12.png")

hw3/hw3_9.py

@@ -1,51 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-
-
-def generate_data(N):
-    y = np.random.choice([1, -1], N)
-
-
-    x = np.empty((N, 3))
-    for index, i in enumerate(y):
-        if i == 1:
-            mean = [3, 2]
-            covariance = [[0.4, 0], [0, 0.4]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-        else:
-            mean = [5, 0]
-            covariance = [[0.6, 0], [0, 0.6]]
-            x1, x2 = np.random.multivariate_normal(mean, covariance)
-            x[index] = np.array([1, x1, x2])
-    return x, y
-
-def average_square_error(y, y_hat):
-    error = y-y_hat
-    error = error ** 2
-    return error.sum()/error.shape[0]
-
-if __name__ == '__main__':
-    errors = []
-    for times in range(128):
-        np.random.seed(times)
-        train_x, train_y = generate_data(256)               # (256, 3), (256, )
-        test_x, test_y = generate_data(4096)
-
-        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
-        w = pseudo_inverse_x @ train_y                      # (3)
-        
-        predict_y = train_x @ w
-
-        error = average_square_error(predict_y, train_y)
-        errors.append(error)
-        print(times, error)
-    
-    errors = sorted(errors)
-    median = ( errors[63] + errors[64] ) / 2 
-
-    plt.hist(errors, bins=10)
-    plt.xlabel("Ein")
-    plt.title("median: {}".format(median))
-    plt.savefig("9.png")