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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,80 +1,80 @@ | ||
| # -*- coding: utf-8 -*- | ||
| """ | ||
| Created on Thu Feb 14 16:10:34 2018 | ||
| @author: Red | ||
| """ | ||
|
|
||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
|
|
||
| def draw_points(X, labels, title='', figsize=(4,4), coordinate=False): | ||
| plt.figure(figsize=figsize) | ||
|
|
||
| plt.title(title) | ||
| plt.xlabel("x1") | ||
| plt.ylabel("x2") | ||
|
|
||
| # x1 and x2 features | ||
| x1 = X[:, 0] | ||
| x2 = X[:, 1] | ||
|
|
||
| plt.xlim(-10, 10) | ||
| plt.ylim(-10, 10) | ||
|
|
||
| max,min = np.max(labels), np.min(labels) | ||
| plt.scatter(x1[labels == max], x2[labels == max], c='black', marker='o') | ||
| plt.scatter(x1[labels == min], x2[labels == min], c='black', marker='o') | ||
|
|
||
| circle = plt.Circle((0, 0), radius=1.1, fill=False, color='red') | ||
| plt.gca().add_patch(circle) | ||
|
|
||
| if coordinate: | ||
| for index, x, y in zip(range(len(labels)), x1, x2): | ||
| plt.annotate('(%.2f,%.2f)'%(x,y), xy=(x,y), xytext=(-20,-20), | ||
| textcoords = 'offset pixels', ha='left', va='bottom') | ||
|
|
||
| return plt | ||
|
|
||
| # generate noraml distribution train set | ||
| def normal_dis_trainset(positive=100, negtive=100, type='normal'): | ||
| np.random.seed(0) | ||
|
|
||
| if type == 'normal': | ||
| numA = np.random.normal(3, 2, (2, positive)) | ||
| numB = np.random.normal(-6, 2, (2, negtive)) | ||
| elif type == 'ones': | ||
| numA = np.ones((2, positive)) - 3 | ||
| numB = np.ones((2, negtive)) + 5 | ||
| else: | ||
| numA = np.zeros((2, positive)) - 3 | ||
| numB = np.zeros((2, negtive)) + 5 | ||
|
|
||
| Ax, Ay = numA[0] * 0.5, numA[1] | ||
| Bx, By = numB[0], numB[1] | ||
|
|
||
| labels = np.zeros((negtive + positive, 1)) | ||
| trainset = np.zeros((negtive + positive, 2)) | ||
| trainset[0:positive,0] = Ax[:] | ||
| trainset[0:positive,1] = Ay[:] | ||
| labels[0:positive] = 1 | ||
|
|
||
| trainset[positive:,0] = Bx[:] | ||
| trainset[positive:,1] = By[:] | ||
| labels[positive:] = -1 | ||
|
|
||
| return trainset, labels.reshape(positive + negtive,) | ||
|
|
||
| def data_split(X, y, ratio=0.3, random_state=0): | ||
| from sklearn.model_selection import train_test_split | ||
|
|
||
| # 'X_train, X_test, y_train, y_test = ' | ||
| return train_test_split(X, y, test_size=ratio, random_state=random_state) | ||
|
|
||
| if __name__ == "__main__": | ||
| X,y = normal_dis_trainset(3, 3) | ||
|
|
||
| X_train, X_test, y_train, y_test = data_split(X, y) | ||
| print(y_train) | ||
| print(y_test) | ||
| # -*- coding: utf-8 -*- | ||
| """ | ||
| Created on Thu Feb 14 16:10:34 2018 | ||
| @author: Red | ||
| """ | ||
|
|
||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
|
|
||
| def draw_points(X, labels, title='', figsize=(4,4), coordinate=False): | ||
| plt.figure(figsize=figsize) | ||
|
|
||
| plt.title(title) | ||
| plt.xlabel("x1") | ||
| plt.ylabel("x2") | ||
|
|
||
| # x1 and x2 features | ||
| x1 = X[:, 0] | ||
| x2 = X[:, 1] | ||
|
|
||
| plt.xlim(-10, 10) | ||
| plt.ylim(-10, 10) | ||
|
|
||
| max,min = np.max(labels), np.min(labels) | ||
| plt.scatter(x1[labels == max], x2[labels == max], c='black', marker='o') | ||
| plt.scatter(x1[labels == min], x2[labels == min], c='black', marker='o') | ||
|
|
||
| circle = plt.Circle((0, 0), radius=1.1, fill=False, color='red') | ||
| plt.gca().add_patch(circle) | ||
|
|
||
| if coordinate: | ||
| for index, x, y in zip(range(len(labels)), x1, x2): | ||
| plt.annotate('(%.2f,%.2f)'%(x,y), xy=(x,y), xytext=(-20,-20), | ||
| textcoords = 'offset pixels', ha='left', va='bottom') | ||
|
|
||
| return plt | ||
|
|
||
| # generate noraml distribution train set | ||
| def normal_dis_trainset(positive=100, negtive=100, type='normal'): | ||
| np.random.seed(0) | ||
|
|
||
| if type == 'normal': | ||
| numA = np.random.normal(3, 2, (2, positive)) | ||
| numB = np.random.normal(-6, 2, (2, negtive)) | ||
| elif type == 'ones': | ||
| numA = np.ones((2, positive)) - 3 | ||
| numB = np.ones((2, negtive)) + 5 | ||
| else: | ||
| numA = np.zeros((2, positive)) - 3 | ||
| numB = np.zeros((2, negtive)) + 5 | ||
|
|
||
| Ax, Ay = numA[0] * 0.5, numA[1] | ||
| Bx, By = numB[0], numB[1] | ||
|
|
||
| labels = np.zeros((negtive + positive, 1)) | ||
| trainset = np.zeros((negtive + positive, 2)) | ||
| trainset[0:positive,0] = Ax[:] | ||
| trainset[0:positive,1] = Ay[:] | ||
| labels[0:positive] = 1 | ||
|
|
||
| trainset[positive:,0] = Bx[:] | ||
| trainset[positive:,1] = By[:] | ||
| labels[positive:] = -1 | ||
|
|
||
| return trainset, labels.reshape(positive + negtive,) | ||
|
|
||
| def data_split(X, y, ratio=0.3, random_state=0): | ||
| from sklearn.model_selection import train_test_split | ||
|
|
||
| # 'X_train, X_test, y_train, y_test = ' | ||
| return train_test_split(X, y, test_size=ratio, random_state=random_state) | ||
|
|
||
| if __name__ == "__main__": | ||
| X,y = normal_dis_trainset(3, 3) | ||
|
|
||
| X_train, X_test, y_train, y_test = data_split(X, y) | ||
| print(y_train) | ||
| print(y_test) | ||
|
|
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