Perceptron algorithm and its Python implementation v2 0

Time:2022-1-6

import matplotlib.pyplot as plt
import numpy as np
from random import uniform, seed, shuffle ,sample
import math
import logging

from random import random

”’JY_Toolkit.py”’
class Jy_makeDataset(object):

def random_state(random_seed):
    seed(int(random_seed))
def draw_ Halfmoon (n_sample: int = 1000, # number of sample points, n_samples in total for the two classifications
                  w: Float = 1, # half month line width
                  Radius: float = 4, # half moon radius
                  hor_distance: float = 4,   # Horizontal direction distance for two point
                  ver_distance: float = 0,   # Vertical direction distance for two point
                  Slope: float = 0, # half moon tilt angle [0 ~ 180]
                  positive_val: int = 1,
                  negative_val: int = -1,
                  ):
    slope %= 180            # make the `slope`  between 0 and 180
    #Will n_ Sample and sample are divided into two categories, each sample n_ Sample / type 2
    each_m = n_sample//2
    # circle origin point of positive moon [x , y]
    p_origin = [1 + w/2 + radius, 1 + w/2 + radius + ver_distance]
    # circle origin point of negative moon [x , y]
    n_origin = [p_origin[0] + hor_distance, p_origin[1] - ver_distance]
    # product positive point
    p_sample = []
    n_sample = []
    for i in range(each_m):
        # Randomly generate l
        temp_l = radius + uniform(-(w/2), w/2)
        # Randomly generate angle i.e. theta
        temp_angle = uniform(slope, slope + 180)
        point_x = p_origin[0] + temp_l*math.cos(math.pi/180*temp_angle)
        point_y = p_origin[1] + temp_l*math.sin(math.pi/180*temp_angle)
        p_sample.append([point_x, point_y, positive_val])
    for i in range(each_m):
        # Randomly generate l
        temp_l = radius + uniform(-(w/2), w/2)
        # Randomly generate angle i.e. theta , but the angle of negative point should between `slope + 180` and `slope + 360`
        temp_angle = uniform(slope + 180, slope + 360)
        point_x = n_origin[0] + temp_l*math.cos(math.pi/180*temp_angle)
        point_y = n_origin[1] + temp_l*math.sin(math.pi/180*temp_angle)
        n_sample.append([point_x, point_y, negative_val])
    sample_points = p_sample + n_sample
    shuffle(sample_points)
    sample_points = np.array(sample_points)
    return sample_points[:, 0:2], sample_points[:, 2]
pass

class Jy_dataSetProcess(object):

def Jy_train_test_split(X,
                        y,
                        test_size : 0.2,
                        ):
    data = np.column_stack((X,y))
    if test_ Size > = [perfectmoney Download]( https://www.gendan5.com/wallet/PerfectMoney.html ) 1 and test_ size <= 0:
        logging.exception('test_size must be greater than 0 less than 1, we will assign test_size value of 0.2')
        test_size = 0.2
    sample_count = int(len(data)*test_size)
    '''
    Separation idea:
    First scramble the input data set, and then take the previous test_ The size part is the test set, and the latter part is the training set
    '''
    shuffle(data)
    X_test = data[0:sample_count-1]
    X_train = data[sample_count:]
    return X_train[:,0:2],  X_test[:,0:2] ,X_train[:,2] , X_test[:,2]
pass

if name == ‘__main__’:

random_seed = 52
Jy_makeDataset.random_state(random_seed)
np_data, label = Jy_makeDataset.draw_HalfMoon(n_sample=2000)
p_point_x1 = [np_data[i][0] for i in range(len(np_data)) if label[i] == 1]
p_point_x2 = [np_data[i][1] for i in range(len(np_data)) if label[i] == 1]
n_point_x1 = [np_data[i][0] for i in range(len(np_data)) if label[i] == -1]
n_point_x2 = [np_data[i][1] for i in range(len(np_data)) if label[i] == -1]
fig = plt.figure(num="HalfMoons", figsize=(8, 8))
ax1 = fig.add_subplot(111)
ax1.scatter(p_point_x1, p_point_x2, c='red')
ax1.scatter(n_point_x1, n_point_x2, c='blue')
plt.show()
print(np_data)