[add]add class to classify iris flowers

python3/iris.data

@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+

python3/perceptron.py

@@ -53,7 +53,7 @@ def _update_weights(self, input_vec, output, label, rate):
         # 把input_vec[x1,x2,x3,...]和weights[w1,w2,w3,...]打包在一起
         # 变成[(x1,w1),(x2,w2),(x3,w3),...]
         # 然后利用感知器规则更新权重
-        print("test", input_vec, output, label, "rate", rate)
+        print(input_vec, output, label, "rate", rate)
         delta = label - output
         self.weights = list(map(
             lambda x_w: rate * delta * x_w[0] + x_w[1],
@@ -79,7 +79,7 @@ def get_training_dataset():
     input_vecs = [[1, 1], [0, 0], [1, 0], [0, 1]]
     # 期望的输出列表，注意要与输入一一对应
     # [1,1] -> 1, [0,0] -> 0, [1,0] -> 0, [0,1] -> 0
-    labels = [1, 0, 0, 0]
+    labels = [1, 0, 1, 1]
     return input_vecs, labels
 
 
@@ -100,7 +100,7 @@ def train_and_perceptron():
     # 训练and感知器
     and_perception = train_and_perceptron()
     print(and_perception)
-    print(and_perception.predict([0, 0]))
+    print("input value{0}, predict:{1}".format([0, 0], and_perception.predict([0, 0])))
     print(and_perception.predict([1, 0]))
     print(and_perception.predict([0, 1]))
     print(and_perception.predict([1, 1]))

python3/perceptron_flower.py

@@ -0,0 +1,95 @@
+# !/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+import pandas as pd
+
+from python3.perceptron import Perceptron
+
+
+# 定义激活函数f
+def active_function(x):
+    # return x
+    if x < 0.5:
+        return 0
+    elif x > 0.5 and x < 2:
+        return 1
+    elif x > 2:
+        return 2
+
+
+class LinearUnit(Perceptron):
+    def __init__(self, input_num):
+        '''初始化线性单元，设置输入参数的个数'''
+        Perceptron.__init__(self, input_num, activator_fun=active_function)
+
+
+def get_training_dataset():
+    '''
+    捏造5个人的收入数据
+    '''
+    # 构建训练数据
+    # 输入向量列表，每一项是工作年限
+    data = pd.read_csv('iris.data', header=None)
+    # take [0, 1] columns of data to be x, take 4 column and do categorical codes as y.
+    # y now is an array composed by 0 (stands for Iris-setosa), 1 (stands for Iris-versicolor), and
+    # 2 (stands for Iris-virginica)
+    x, y = data[[0, 1, 2, 3]], pd.Categorical(data[4]).codes
+    # pd.to_list
+
+    # print(x.values.tolist())
+    input_vecs = [x.values.tolist()][0]
+    labels = y.tolist()
+    print(type(labels), labels)
+    # labels = y.
+    print(input_vecs)
+    # 期望的输出列表，月薪，注意要与输入一一对应
+    # labels = [5500, 2300, 7600, 1800, 11400]
+    return input_vecs, labels
+
+
+def train_linear_unit():
+    '''
+    使用数据训练线性单元
+    '''
+    # 创建感知器，输入参数的特征数为1（工作年限）
+    lu = LinearUnit(4)
+    # 训练，迭代10轮, 学习速率为0.01
+    input_vecs, labels = get_training_dataset()
+    lu.train(input_vecs, labels, 10000, 0.001)
+    # 返回训练好的线性单元
+    return lu
+
+
+# def plot(linear_unit):
+#     import matplotlib.pyplot as plt
+#     input_vecs, labels = get_training_dataset()
+#     fig = plt.figure()
+#     ax = fig.add_subplot(111)
+#     ax.scatter(map(lambda x: x[0], input_vecs), labels)
+#     weights = linear_unit.weights
+#     bias = linear_unit.bias
+#     x = range(0,12,1)
+#     y = map(lambda x:weights[0] * x + bias, x)
+#     ax.plot(x, y)
+#     plt.show()
+
+
+if __name__ == '__main__':
+    '''训练线性单元'''
+
+    # data = pd.read_csv('iris.data', header=None)
+    # take [0, 1] columns of data to be x, take 4 column and do categorical codes as y.
+    # y now is an array composed by 0 (stands for Iris-setosa), 1 (stands for Iris-versicolor), and
+    # 2 (stands for Iris-virginica)
+    # x, y = data[[0, 1, 2, 3]], pd.Categorical(data[4]).codes
+    # pd.to_list
+
+    # print(x, y)
+    linear_unit = train_linear_unit()
+    print("predict")
+    print(linear_unit.predict([5.1, 3.5, 1.4, 0.2]))
+    print(linear_unit.predict([5.9, 3.0, 5.1, 1.8]))
+    print(linear_unit.predict([6.0,2.2,5.0,1.5]))
+    print(linear_unit.predict([6.0, 3.4, 4.5, 1.6]))
+
+    # 打印训练获得的权重