感知機(jī)是一種較為簡單的二分類模型，感知機(jī)旨在學(xué)習(xí)能夠?qū)⑤斎霐?shù)據(jù)劃分為+1/-1的線性分離超平面，所以說整體而言感知機(jī)是一種線性模型。?

查看數(shù)據(jù)集

import pandas as pd import numpy as np from sklearn.datasets import load_iris import matplotlib.pyplot as plt # load data iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) df['label'] = iris.targetdf.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label'] print(df.label.value_counts())plt.scatter(df[:50]['sepal length'], df[:50]['sepal width'], label='one') plt.scatter(df[50:100]['sepal length'], df[50:100]['sepal width'], label='two') plt.xlabel('sepal length') plt.ylabel('sepal width') plt.legend() plt.show()

?sepal length (cm) ?sepal width (cm) ?petal length (cm) ?petal width (cm)

發(fā)現(xiàn)四個維度的數(shù)據(jù),有兩個維度就可以線性可分.

import pandas as pd import numpy as np from sklearn.datasets import load_iris import matplotlib.pyplot as plt # 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'] # print(df.label.value_counts()) # # plt.scatter(df[:50]['sepal length'], df[:50]['sepal width'], label='one') # plt.scatter(df[50:100]['sepal length'], df[50:100]['sepal width'], label='two') # plt.xlabel('sepal length') # plt.ylabel('sepal width') # plt.legend() # plt.show()data = np.array(df.iloc[:100, [0,1,-1]]) print(data) X, y = data[:,:-1], data[:,-1] print(type(X)) y = np.array([1 if i == 1 else -1 for i in y])# 數(shù)據(jù)線性可分，二分類數(shù)據(jù) # 此處為一元一次線性方程 class Model:def __init__(self):self.w = np.ones(len(data[0]) - 1, dtype=np.float32)print('self.w',self.w)self.b = 0self.l_rate = 0.1# self.data = datadef sign(self, x, w, b):y = np.dot(x, w) + breturn y# 隨機(jī)梯度下降法def fit(self, X_train, y_train):is_wrong = Falsewhile not is_wrong:wrong_count = 0for d in range(len(X_train)):X = X_train[d]y = y_train[d]if y * self.sign(X, self.w, self.b) <= 0:self.w = self.w + self.l_rate * np.dot(y, X)self.b = self.b + self.l_rate * ywrong_count += 1if wrong_count == 0:is_wrong = Truereturn 'Perceptron Model!'def score(self):passperceptron = Model() perceptron.fit(X, y)x_points = np.linspace(4, 7,10) print('x_points=',x_points) 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='one') plt.plot(data[50:100, 0], data[50:100, 1], 'bo', color='orange', label='two') plt.xlabel('sepal length') plt.ylabel('sepal width') plt.legend() plt.show()

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