# coding=utf-8
from math import exp

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets.samples_generator import make_blobs


def sigmoid(num):
      return 1.0 / (1 + exp(-1 * num))


class logistic():
    def __init__(self, x, y):
        if type(x) == type(y) == list:
            self.x = np.array(x)
            self.y = np.array(y)
        elif type(x) == type(y) == np.ndarray:
            self.x = x
            self.y = y
        else:
           print("初始化错误")




    def sigmoid(self, x):
        s = np.frompyfunc(lambda x: sigmoid(x), 1, 1)
        return s(x)

    def train_with_punish(self, learnrate, errors, punish=0.0001):
        self.punish = punish
        dimension = self.x.shape[1]
        self.theta = np.random.random(dimension)
        compute_error = 100000000
        times = 0
        while compute_error > errors:
            res = np.dot(self.x, self.theta)
            delta = self.sigmoid(res) - self.y
            self.theta = self.theta - learnrate * np.dot(self.x.T, delta) - punish * self.theta  # 带惩罚的梯度下降方法
            compute_error = np.sum(delta)
            times += 1

    def predict(self, x):
        x = np.array(x)
        if self.sigmoid(np.dot(x, self.theta)) > 0.5:
            return 1
        else:
            return 0


def test1():

    #随机生成数据
    x, y = make_blobs(n_samples=200, centers=2, n_features=2, random_state=0, center_box=(10, 20))
    x1=[]
    y1 = []
    x2=[]
    y2 = []
    for i in range(len(y)):
        if y[i] == 0:
            x1.append(x[i][0])
            y1.append(x[i][1])
        elif y[i] == 1:
            x2.append(x[i][0])
            y2.append(x[i][1])
    p = logistic(x, y)
    p.train_with_punish(learnrate=0.00001, errors=0.005, punish=0.01)  # 步长是0.00001，最大允许误差是0.005，惩罚系数是0.01
    x_test = np.arange(10, 20, 0.01)
    y_test = (-1 * p.theta[0] / p.theta[1]) * x_test
    plt.plot(x_test, y_test, c='g', label='logistic_line')
    plt.scatter(x1, y1, c='r', label='pos')
    plt.scatter(x2, y2, c='b', label='neg')
    plt.legend(loc=2)
    plt.title('punish ：' + p.punish.__str__())
    plt.xlabel('X')
    plt.title('Grad with reg')
    plt.ylabel('Y')
    plt.show()

test1()
5c2bd0eebb3b238d6690c0a96ebcf0d7c5e8533b
<mask token>


class logistic:
    <mask token>
    <mask token>

    def train_with_punish(self, learnrate, errors, punish=0.0001):
        self.punish = punish
        dimension = self.x.shape[1]
        self.theta = np.random.random(dimension)
        compute_error = 100000000
        times = 0
        while compute_error > errors:
            res = np.dot(self.x, self.theta)
            delta = self.sigmoid(res) - self.y
            self.theta = self.theta - learnrate * np.dot(self.x.T, delta
                ) - punish * self.theta
            compute_error = np.sum(delta)
            times += 1

    def predict(self, x):
        x = np.array(x)
        if self.sigmoid(np.dot(x, self.theta)) > 0.5:
            return 1
        else:
            return 0


<mask token>

<mask token>


class logistic:

    def __init__(self, x, y):
        if type(x) == type(y) == list:
            self.x = np.array(x)
            self.y = np.array(y)
        elif type(x) == type(y) == np.ndarray:
            self.x = x
            self.y = y
        else:
            print('初始化错误')

    def sigmoid(self, x):
        s = np.frompyfunc(lambda x: sigmoid(x), 1, 1)
        return s(x)

    def train_with_punish(self, learnrate, errors, punish=0.0001):
        self.punish = punish
        dimension = self.x.shape[1]
        self.theta = np.random.random(dimension)
        compute_error = 100000000
        times = 0
        while compute_error > errors:
            res = np.dot(self.x, self.theta)
            delta = self.sigmoid(res) - self.y
            self.theta = self.theta - learnrate * np.dot(self.x.T, delta
                ) - punish * self.theta
            compute_error = np.sum(delta)
            times += 1

    def predict(self, x):
        x = np.array(x)
        if self.sigmoid(np.dot(x, self.theta)) > 0.5:
            return 1
        else:
            return 0


<mask token>

<mask token>


def sigmoid(num):
    return 1.0 / (1 + exp(-1 * num))


class logistic:

    def __init__(self, x, y):
        if type(x) == type(y) == list:
            self.x = np.array(x)
            self.y = np.array(y)
        elif type(x) == type(y) == np.ndarray:
            self.x = x
            self.y = y
        else:
            print('初始化错误')

    def sigmoid(self, x):
        s = np.frompyfunc(lambda x: sigmoid(x), 1, 1)
        return s(x)

    def train_with_punish(self, learnrate, errors, punish=0.0001):
        self.punish = punish
        dimension = self.x.shape[1]
        self.theta = np.random.random(dimension)
        compute_error = 100000000
        times = 0
        while compute_error > errors:
            res = np.dot(self.x, self.theta)
            delta = self.sigmoid(res) - self.y
            self.theta = self.theta - learnrate * np.dot(self.x.T, delta
                ) - punish * self.theta
            compute_error = np.sum(delta)
            times += 1

    def predict(self, x):
        x = np.array(x)
        if self.sigmoid(np.dot(x, self.theta)) > 0.5:
            return 1
        else:
            return 0


def test1():
    x, y = make_blobs(n_samples=200, centers=2, n_features=2, random_state=
        0, center_box=(10, 20))
    x1 = []
    y1 = []
    x2 = []
    y2 = []
    for i in range(len(y)):
        if y[i] == 0:
            x1.append(x[i][0])
            y1.append(x[i][1])
        elif y[i] == 1:
            x2.append(x[i][0])
            y2.append(x[i][1])
    p = logistic(x, y)
    p.train_with_punish(learnrate=1e-05, errors=0.005, punish=0.01)
    x_test = np.arange(10, 20, 0.01)
    y_test = -1 * p.theta[0] / p.theta[1] * x_test
    plt.plot(x_test, y_test, c='g', label='logistic_line')
    plt.scatter(x1, y1, c='r', label='pos')
    plt.scatter(x2, y2, c='b', label='neg')
    plt.legend(loc=2)
    plt.title('punish ：' + p.punish.__str__())
    plt.xlabel('X')
    plt.title('Grad with reg')
    plt.ylabel('Y')
    plt.show()


<mask token>

from math import exp
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets.samples_generator import make_blobs


def sigmoid(num):
    return 1.0 / (1 + exp(-1 * num))


class logistic:

    def __init__(self, x, y):
        if type(x) == type(y) == list:
            self.x = np.array(x)
            self.y = np.array(y)
        elif type(x) == type(y) == np.ndarray:
            self.x = x
            self.y = y
        else:
            print('初始化错误')

    def sigmoid(self, x):
        s = np.frompyfunc(lambda x: sigmoid(x), 1, 1)
        return s(x)

    def train_with_punish(self, learnrate, errors, punish=0.0001):
        self.punish = punish
        dimension = self.x.shape[1]
        self.theta = np.random.random(dimension)
        compute_error = 100000000
        times = 0
        while compute_error > errors:
            res = np.dot(self.x, self.theta)
            delta = self.sigmoid(res) - self.y
            self.theta = self.theta - learnrate * np.dot(self.x.T, delta
                ) - punish * self.theta
            compute_error = np.sum(delta)
            times += 1

    def predict(self, x):
        x = np.array(x)
        if self.sigmoid(np.dot(x, self.theta)) > 0.5:
            return 1
        else:
            return 0


def test1():
    x, y = make_blobs(n_samples=200, centers=2, n_features=2, random_state=
        0, center_box=(10, 20))
    x1 = []
    y1 = []
    x2 = []
    y2 = []
    for i in range(len(y)):
        if y[i] == 0:
            x1.append(x[i][0])
            y1.append(x[i][1])
        elif y[i] == 1:
            x2.append(x[i][0])
            y2.append(x[i][1])
    p = logistic(x, y)
    p.train_with_punish(learnrate=1e-05, errors=0.005, punish=0.01)
    x_test = np.arange(10, 20, 0.01)
    y_test = -1 * p.theta[0] / p.theta[1] * x_test
    plt.plot(x_test, y_test, c='g', label='logistic_line')
    plt.scatter(x1, y1, c='r', label='pos')
    plt.scatter(x2, y2, c='b', label='neg')
    plt.legend(loc=2)
    plt.title('punish ：' + p.punish.__str__())
    plt.xlabel('X')
    plt.title('Grad with reg')
    plt.ylabel('Y')
    plt.show()


test1()

# coding=utf-8
from math import exp

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets.samples_generator import make_blobs


def sigmoid(num):
      return 1.0 / (1 + exp(-1 * num))


class logistic():
    def __init__(self, x, y):
        if type(x) == type(y) == list:
            self.x = np.array(x)
            self.y = np.array(y)
        elif type(x) == type(y) == np.ndarray:
            self.x = x
            self.y = y
        else:
           print("初始化错误")




    def sigmoid(self, x):
        s = np.frompyfunc(lambda x: sigmoid(x), 1, 1)
        return s(x)

    def train_with_punish(self, learnrate, errors, punish=0.0001):
        self.punish = punish
        dimension = self.x.shape[1]
        self.theta = np.random.random(dimension)
        compute_error = 100000000
        times = 0
        while compute_error > errors:
            res = np.dot(self.x, self.theta)
            delta = self.sigmoid(res) - self.y
            self.theta = self.theta - learnrate * np.dot(self.x.T, delta) - punish * self.theta  # 带惩罚的梯度下降方法
            compute_error = np.sum(delta)
            times += 1

    def predict(self, x):
        x = np.array(x)
        if self.sigmoid(np.dot(x, self.theta)) > 0.5:
            return 1
        else:
            return 0


def test1():

    #随机生成数据
    x, y = make_blobs(n_samples=200, centers=2, n_features=2, random_state=0, center_box=(10, 20))
    x1=[]
    y1 = []
    x2=[]
    y2 = []
    for i in range(len(y)):
        if y[i] == 0:
            x1.append(x[i][0])
            y1.append(x[i][1])
        elif y[i] == 1:
            x2.append(x[i][0])
            y2.append(x[i][1])
    p = logistic(x, y)
    p.train_with_punish(learnrate=0.00001, errors=0.005, punish=0.01)  # 步长是0.00001，最大允许误差是0.005，惩罚系数是0.01
    x_test = np.arange(10, 20, 0.01)
    y_test = (-1 * p.theta[0] / p.theta[1]) * x_test
    plt.plot(x_test, y_test, c='g', label='logistic_line')
    plt.scatter(x1, y1, c='r', label='pos')
    plt.scatter(x2, y2, c='b', label='neg')
    plt.legend(loc=2)
    plt.title('punish ：' + p.punish.__str__())
    plt.xlabel('X')
    plt.title('Grad with reg')
    plt.ylabel('Y')
    plt.show()

test1()