统计学习方法：感知机

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字数统计: 571 | 阅读时长 ≈ 3

注：代码和运行结果都是从jupyter notebook中导出的

导入所需要的第三方库

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import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
%matplotlib inline

导入数据（iris数据集是sklearn中自带的数据集）

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# load data
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['label'] = iris.target
df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']
df.label.value_counts()

2    50
1    50
0    50
Name: label, dtype: int64

选取两个特征sepal length和sepal width，绘制散点图

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plt.scatter(df[:50]['sepal length'], df[:50]['sepal width'], label='0')
plt.scatter(df[50:100]['sepal length'], df[50:100]['sepal width'], label='1')
plt.xlabel('sepal length')
plt.ylabel('sepal width')
plt.legend()

<matplotlib.legend.Legend at 0x1b17e936160>

png

数据处理：将pandas类型数据转换为np.array

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data = np.array(df.iloc[:100, [0, 1, -1]])
X, y = data[:, :-1], data[:, -1]
y = np.array([1 if i == 1. else -1 for i in y])

定义模型

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class Model():
    def __init__(self):
        self.w = np.ones(len(data[0])-1, dtype=np.float32)
        self.b = 0
        self.l_rate = 0.1
    def linear_func(self, w, b, x):
        return np.dot(x, w) + b
    def fit(self, X_train, y_train):
        finished = False
        while not finished:
            wrong_count = 0
            for d in range(len(X_train)):
                if y_train[d] * self.linear_func(self.w, self.b, X_train[d]) <= 0:
                    self.w = self.w + self.l_rate * y_train[d] * X_train[d]
                    self.b = self.b + self.l_rate * y_train[d]
                    wrong_count += 1
            if wrong_count == 0:
                finished = True
        return "perceptron model"

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perceptron = Model()
perceptron.fit(X, y)
print(perceptron.w)
print(perceptron.b)
[  7.8 -10. ]
-12.099999999999973

使用自己训练的模型做二分类

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x_points = np.linspace(4, 7, 10)
y_ = -(perceptron.w[0] * x_points + perceptron.b) / perceptron.w[1]
plt.plot(x_points, y_)

plt.plot(data[:50, 0], data[:50, 1], 'bo', color='blue', label='0')
plt.plot(data[50:100, 0], data[50:100, 1], 'bo', color='orange', label='1')
plt.legend()

<matplotlib.legend.Legend at 0x1b17ecb4d68>

png

使用sklearn中自带的感知机模型

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from sklearn.linear_model import Perceptron
clf = Perceptron(fit_intercept=False, max_iter=1000, shuffle=False)
clf.fit(X, y)

print(clf.coef_)
print(clf.intercept_)

[[  74.6 -127.2]]
[0.]


c:\python36\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in Perceptron in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.
  FutureWarning)

绘制sklearn训练得到的感知机模型

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x_points = np.arange(4, 8)
y_ = -(clf.coef_[0][0] * x_points + clf.intercept_) / clf.coef_[0][1]
plt.plot(x_points, y_)

plt.plot(data[:50, 0], data[:50, 1], 'bo', color='blue', label='0')
plt.plot(data[50:100, 0], data[50:100, 1], 'bo', color='orange', label='1')
plt.legend()

<matplotlib.legend.Legend at 0x1b17f046278>

png

C:\Python36\Lib\site-packages\sklearn\datasets\data

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