def __init__(self, mean, cov, N, cls, maxcls):
self.N = int(N)
self.cls = cls
# 2次元正規分布に従ってデータ生成(ヘンにクラスを使いすぎた)
gauss2d = gen.GenerateRandomNoize2D(mean=mean, cov=cov, n=N)
gauss2d.generate()
# 生成した座標を格納
self.x1 = np.reshape(gauss2d.x, (len(gauss2d.x), 1))
self.x2 = np.reshape(gauss2d.y, (len(gauss2d.y), 1))
self.x_vec = np.hstack((self.x1, self.x2))
def __init__(self, mean, cov, N, cls, maxcls):
self.N = N
# 1-of-K 符号化によってクラスを表すベクトルを生成
self.clsvec = np.zeros(maxcls)
self.clsvec[cls - 1] = 1
# 2次元正規分布に従ってデータ生成(ヘンにクラスを使いすぎた)
gauss2d = gen.GenerateRandomNoize2D(mean=mean, cov=cov, n=N)
gauss2d.generate()
# 生成した座標を格納
self.x1 = np.reshape(gauss2d.x, (len(gauss2d.x), 1))
self.x2 = np.reshape(gauss2d.y, (len(gauss2d.y), 1))
self.x_vec = np.hstack((self.x1, self.x2))
# coding:utf-8
import matplotlib.pyplot as plt
import numpy as np
import random as rd
import sys
from scipy.stats import norm
sys.path.append('../sampledata')
import generate_data as gen
mean = np.array([0, 0])
cov = np.array([[2, -1.5], [-1.5, 2]])
gauss2d = gen.GenerateRandomNoize2D(mean=mean, cov=cov, n=50)
gauss2d.generate()
plt.scatter(x=gauss2d.x, y=gauss2d.y, alpha=0.4, color='red')
plt.xlim((-4, 10))
plt.ylim((-4, 10))
plt.show()