sigmoid函数python画图代码_逻辑回归代码-python from __future__ import print_function, division
import sys
import os
import math
from sklearn import datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def shuffle_data(X, y, seed=None):
if seed:
np.random.seed(seed)
idx = np.arange(X.shape[0])
np.random.shuffle(idx)
return X[idx], y[idx]
# 正规化数据集 X
def normalize(X, axis=-1, p=2):
lp_norm = np.atleast_1d((X, p, axis))
lp_norm[lp_norm == 0] = 1
return X / np.expand_dims(lp_norm, axis)
# 标准化数据集 X
def standardize(X):
X_std = np.zeros(X.shape)
mean = X.mean(axis=0)
std = X.std(axis=0)
# 做除法运算时请永远记住分母不能等于0的情形
# X_std = (X - X.mean(axis=0)) / X.std(axis=0)
for col in range(np.shape(X)[1]):
if std[col]:
X_std[:, col] = (X_std[:, col] - mean[col]) / std[col]
return X_std
# 划分数据集为训练集和测试集
def train_test_split(X, y, test_size=0.3, shuffle=True, seed=None):
if shuffle:
X, y = shuffle_data(X, y, seed)
n_train_samples = int(X.shape[0] * (1-test_size))
python新手代码画图
x_train, x_test = X[:n_train_samples], X[n_train_samples:] y_train, y_test = y[:n_train_samples], y[n_train_samples:] return x_train, x_test, y_train, y_test
# 将⼀个向量转换成对⾓阵，其中对⾓阵上的元素就是向量中元素def vec2diagonal(vec):
vec_length = len(vec)
diagonal = np.zeros((vec_length, vec_length))
for i in range(vec_length):
diagonal[i][i] = vec[i]
return diagonal
def accuracy(y, y_pred):
y = y.reshape(y.shape[0], -1)
y_pred = shape(y_pred.shape[0], -1)
return np.sum(y == y_pred)/len(y)
class Sigmoid:
def function(self, x):
return 1/(1 + np.exp(-x))
def derivative(self, x):
return self.function(x) * (1 - self.function(x))
class LogisticRegression():
#逻辑回归分类模型.
def __init__(self, learning_rate=.1):
self.w = None
self.learning_rate = learning_rate
self.sigmoid = Sigmoid()
def fit(self, X, y, n_iterations=4000):
# 在第⼀列添加偏置列，全部初始化为1
X = np.insert(X, 0, 1, axis=1)
X = X.reshape(X.shape[0], -1)
y = y.reshape(y.shape[0], -1)
n_samples, n_features = np.shape(X)
# 参数初始化 [-1/n_features, 1/n_features]
limit = 1 / math.sqrt(n_features)
self.w = np.random.uniform(-limit, limit, (n_features, 1))
for i in range(n_iterations):
# 通过初始化的参数w计算预测值
y_pred = self.sigmoid.function(X.dot(self.w))
# 梯度下降更新参数w.
self.w -= self.learning_rate * X.T.dot(-(y - y_pred) *
self.sigmoid.function(X.dot(self.w)) *
(1 - self.sigmoid.function(X.dot(self.w))))
def predict(self, X):
# 训练模型的时候我们添加了偏置，预测的时候也需要添加偏置
X = X.reshape(X.shape[0], -1)
X = np.insert(X, 0, 1, axis=1)
# 预测
y_pred = np.round(self.sigmoid.function(X.dot(self.w))).astype(int)
return y_pred
def main():
# 加载数据集
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = 0
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1) clf = LogisticRegression()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accu = accuracy(y_test, y_pred)
print ("Accuracy:", accu)
plt.figure(figsize=(12, 8))
plt.scatter(X[y==0][:,0], X[y==0][:,1])
plt.scatter(X[y==1][:,0], X[y==1][:,1])
plt.show()
if __name__ == "__main__":
main()

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