単純パーセプトロンの実装

singlePerceptron.py

# -*- coding: utf-8 -*-

import numpy
import matplotlib.pyplot as plt
	
def predict(w, x):

	output = numpy.dot(w, x)
	
	if output >= 0:
		result = 1
	else:
		result = -1

	return [result, output]

def train(w, x, label):
	[result, output] = predict(w, x)

	learningRate = 0.5

	if output * label < 0:
		# 更新
		return w + learningRate * label * numpy.array(x)
	else:
		return w

if __name__=='__main__':

	area = 100
	loop = 1000
	num = 50
	bias = 1

	# 学習用データ 第一象限、第3象限に大まかにわける
	train_X1 = numpy.random.rand(num) * area
	train_Y1 = numpy.random.rand(num) * area * 0.1

	train_X2 = numpy.random.rand(num) * area - area
	train_Y2 = numpy.random.rand(num) * area - area

	# ラベルづけ
	label1 = numpy.ones(num)
	label2 = -1 * numpy.ones(num)

	dataSet1 = numpy.c_[train_X1, train_Y1, label1]
	dataSet2 = numpy.c_[train_X2, train_Y2, label2]

	allDataSet = numpy.r_[dataSet1, dataSet2]

	# 初期の重みは適当
	weight = [[numpy.random.rand(), -numpy.random.rand(), 1]]

	for j in range(loop):
		for (x, y, label) in allDataSet:
			
			w_new = train(weight[-1], [x, y, bias], label)
			
			weight.append(w_new)
		print(j)
		print(w_new)

	w = weight[-1]

	# 図に描画
	x_fig = range(-area,area)
	y_fig = [-(w[1]/w[0])*xi-(w[2]/w[1]) for xi in x_fig]

	plt.scatter(train_X1, train_Y1, marker='o', color='g', s=100)
	plt.scatter(train_X2, train_Y2, marker='s', color='b', s=100)
	plt.plot(x_fig,y_fig)
	plt.show()