logistic regression fake data version complete

WenDesi · WenDesi · commit d1d43a47a7a4 · 2016-11-08T11:07:52.000+08:00
diff --git a/logistic_regression/logistic_regression.py b/logistic_regression/logistic_regression.py
@@ -0,0 +1,115 @@
+# encoding=utf-8
+# @Author: WenDesi
+# @Date:   08-11-16
+# @Email:  wendesi@foxmail.com
+# @Last modified by:   WenDesi
+# @Last modified time: 08-11-16
+
+import math
+import random
+
+
+def predict_(x, w):
+    wx = sum([w[j] * x[j] for j in xrange(len(w))])
+    exp_wx = math.exp(wx)
+
+    predict1 = exp_wx / (1 + exp_wx)
+    predict0 = 1 / (1 + exp_wx)
+
+    if predict1 > predict0:
+        return 1
+    else:
+        return 0
+
+
+def train(features, labels):
+    w = [0.0] * (len(features[0]) + 1)
+
+    learning_step = 0.00001
+    max_iteration = 1000
+    correct_count = 0
+    time = 0
+
+    while time < max_iteration:
+        index = random.randint(0, len(labels) - 1)
+        x = features[index]
+        x.append(1.0)
+        y = labels[index]
+
+        if y == predict_(x, w):
+            correct_count += 1
+            if correct_count > max_iteration:
+                break
+            continue
+
+        print 'iterater times %d' % time
+        time += 1
+        correct_count = 0
+
+        wx = sum([w[i] * x[i] for i in xrange(len(w))])
+        exp_wx = math.exp(wx)
+
+        for i in xrange(len(w)):
+            w[i] -= learning_step * (-y * x[i] + float(x[i] * exp_wx) / float(1 + exp_wx))
+
+    return w
+
+
+def predict(features, w):
+    labels = []
+
+    for feature in features:
+        feature.append(1)
+        x = feature
+
+        labels.append(predict_(x,w))
+
+    return labels
+
+
+def build_dataset(label, original_posins, radius, size):
+    datasets = []
+    dim = len(original_posins)
+
+    for i in xrange(size):
+        dataset = [label]
+        for j in xrange(dim):
+            point = random.randint(0, 2 * radius) - radius + original_posins[j]
+            dataset.append(point)
+        datasets.append(dataset)
+
+    return datasets
+
+if __name__ == "__main__":
+
+    # 构建训练集
+    trainset1 = build_dataset(0, [0, 0], 10, 100)
+    trainset2 = build_dataset(1, [30, 30], 10, 100)
+
+    trainset = trainset1
+    trainset.extend(trainset2)
+    random.shuffle(trainset)
+
+    trainset_features = map(lambda x: x[1:], trainset)
+    trainset_labels = map(lambda x: x[0], trainset)
+
+    # 训练
+    w = train(trainset_features, trainset_labels)
+
+    # 构建测试集
+    testset1 = build_dataset(0, [0, 0], 10, 500)
+    testset2 = build_dataset(1, [30, 30], 10, 500)
+
+    testset = testset1
+    testset.extend(testset2)
+    random.shuffle(testset)
+
+    testset_features = map(lambda x: x[1:], testset)
+    testset_labels = map(lambda x: x[0], testset)
+
+    # 测试
+    testset_predicts = predict(testset_features, w)
+    print 'asad'
+    accuracy_score = float(len(filter(lambda x: x == True, [testset_labels[i] == testset_predicts[
+                           i] for i in xrange(len(testset_predicts))]))) / float(len(testset_predicts))
+    print "The accruacy socre is ", accuracy_score
