逻辑回归

logistic/test_data

@@ -0,0 +1,100 @@
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logistic/train.py

@@ -0,0 +1,83 @@
+# -*- encoding:utf-8 -*-
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import cross_val_predict
+from numpy import shape
+from sklearn import metrics
+from sklearn.metrics import log_loss
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def read_data(path):
+    with open(path) as f:
+        lines = f.readlines()
+    lines = [eval(line.strip()) for line in lines]
+    X, y = zip(*lines)
+    X = np.array(X)
+    y = np.array(y)
+    return X, y
+
+
+def curve(x_train, w, w0):
+    results = x_train.tolist()
+    for i in range(0, 100):
+        x1 = 1.0 * i / 10
+        x2 = -1 * (w[0] * x1 + w0) / w[1]
+        results.append([x1, x2])
+    results = ["{},{}".format(x1, x2) for [x1, x2] in results]
+    return results
+
+
+def drawScatterAndLine(p, q):
+    x1 = []
+    x2 = []
+    y1 = []
+    y2 = []
+
+    for idx,i in enumerate(q):
+        if i == 0:
+            x1.append(p[idx][0])
+            y1.append(p[idx][1])
+        else:
+            x2.append(p[idx][0])
+            y2.append(p[idx][1])
+
+    plt.scatter(x1, y1)
+    plt.scatter(x2, y2)
+    plt.xlabel('p')
+    plt.ylabel('q')
+    plt.title('line regesion')
+    plt.show()
+
+
+def main():
+    X_train, y_train = read_data("train_data")
+    drawScatterAndLine(X_train, y_train)
+    X_test, y_test = read_data("test_data")
+
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+
+    print "w", model.coef_
+    print "w0", model.intercept_
+
+    y_pred = model.predict(X_test)
+    print y_pred
+    # y_pred = model.predict_proba(X_test)
+    # print y_pred
+    # loss=log_loss(y_test,y_pred)
+    # print "KL_loss:",loss
+    # loss=log_loss(y_pred,y_test)
+    # print "KL_loss:",loss
+    '''
+    curve_results=curve(X_train,model.coef_.tolist()[0],model.intercept_.tolist()[0])
+    with open("train_with_splitline","w") as f :
+        f.writelines("\n".join(curve_results))
+    '''
+
+if __name__ == '__main__':
+    main()
+
+

logistic/train_data

@@ -0,0 +1,500 @@
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