import numpy as np

import pandas as pd

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from random import random

np.random.seed(10)

def makeRandomPoint(num, dim, upper):

return np.random.normal(loc=upper, size=[num, dim])

# 产生一个k维随机向量，每维分量值在0~1之间

def random_point(k):

return [random() for _ in range(k)]

# 产生n个k维随机向量

def random_points(k, n):

return [random_point(k) for _ in range(n)]

# data

def create_logistic_data():

iris = load_iris()

df = pd.DataFrame(iris.data, columns=iris.feature_names)

df['label'] = iris.target

df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']

data = np.array(df.iloc[:100, [0,1,-1]])

X, y = data[:,:2], data[:,-1]

return train_test_split(X, y, test_size=0.3)

def create_svm_data():

iris = load_iris()

df = pd.DataFrame(iris.data, columns=iris.feature_names)

df['label'] = iris.target

df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']

data = np.array(df.iloc[:100, [0, 1, -1]])

for i in range(len(data)):

if data[i, -1] == 0:

data[i, -1] = -1

X, y = data[:,:2], data[:,-1]

return train_test_split(X, y, test_size=0.3)