感知器算法的Python實現

    

感知器算法主要是二值分類問題，以上的圖爲感知器的原理，通過感知器將線性可分的兩個類別區分開來。

以下是感知器算法的Python代碼實現：

import numpy as np
class Perceptron(object):
    """Perceptron classifier.

    Parameters
    ------------
    eta:float
        Learning rate (between 0.0 and 1.0)
    n_iter:int
        Passes over the training dataset.

    Attributes
    -------------
    w_: 1d-array
        Weights after fitting.
    errors_: list
        Numebr of misclassifications in every epoch.

    """

    def __init__(self, eta=0.01, n_iter=10):
        self.eta = eta
        self.n_iter = n_iter

    def fit(self, X, y):
        """Fit training data.

        Parameters
        ------------
        X: {array-like}, shape=[n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_featuers is the number of features.
        y: array-like, shape=[n_smaples]
            Target values.

        Returns
        ----------
        self: object
        """

        self.w_ = np.zeros(1 + X.shape[1]) # Add w_0
        self.errors_ = []

        for _ in range(self.n_iter):
            errors = 0
            for xi, target in zip(X, y):
                update = self.eta * (target - self.predict(xi))
                self.w_[1:] += update * xi
                self.w_[0] += update
                errors += int(update != 0.0)
            self.errors_.append(errors)
        return self

    def net_input(self, X):
        """Calculate net input"""
        return np.dot(X, self.w_[1:]) + self.w_[0]

    def predict(self, X):
        """Return class label after unit step"""
        return np.where(self.net_input(X) >= 0.0, 1, -1) #analoge ? : in C++

以下一個感知器算法的實踐：

#coding:utf-8
#**********************************https://ljalphabeta.gitbooks.io/python-/content/ch2section3.html
#pərˈsepträn
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.colors import ListedColormap
import Perceptron as perceptron_class
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
#print df.tail()

#抽取出前100條樣本，這正好是Setosa和Versicolor對應的樣本，我們將Versicolor對應的數據作爲類別1，Setosa對應的作爲-1。
# 對於特徵，我們抽取出sepal length和petal length兩維度特徵，然後用散點圖對數據進行可視化

y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
# plt.scatter(X[:50, 0], X[:50, 1],color='red', marker='o', label='setosa')
# plt.scatter(X[50:100, 0], X[50:100, 1],color='blue', marker='x', label='versicolor')
# plt.xlabel('sepal length')
# plt.ylabel('petal length')
# plt.legend(loc='upper left')
# plt.show()

#這裏是對於感知機模型進行訓練
ppn = perceptron_class.Perceptron(eta=0.1, n_iter=10)
ppn.fit(X, y)

#畫出分界線
def plot_decision_regions(X, y, classifier, resolution=0.02):
    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    print(xx1)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())
    # plot class samples
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
        alpha=0.8, c=cmap(idx),
        marker=markers[idx], label=cl)

plot_decision_regions(X, y, classifier=ppn)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()

ps：文中的圖片和代碼主要來源網絡以及Sebastian Raschka所著的PYTHON MACHINE LEARNING