svm鸢尾花分类python代码_python实现svm对鸢尾花进行分类（附带鸢尾花数据）

鸢尾花分类

鸢尾花数据链接：

http://bj.bcebos.com/v1/ai-studio-online/93e8a07d6624465c943f60a0b4ec5fd959d44b5e5453410a8b2452ed3720c32f?responseContentDisposition=attachment%3B%20filename%3Diris.data&authorization=bce-auth-v1%2F0ef6765c1e494918bc0d4c3ca3e5c6d1%2F2018-12-12T14%3A57%3A54Z%2F-1%2F%2F2cbe86672d5f2d44278cc3f76789307590c5aeffa85569803fd6e7d625b43ca2

方法一

import numpy as np

from sklearn import model_selection as mo

from sklearn import svm

import matplotlib.pyplot as plt

from matplotlib import colors

import matplotlib as mpl

def iris_type(s):

# 数据转为整型，数据集标签类别由string转为int

it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}

return it[s]

data = np.loadtxt(r'D:\PycharmProjects\untitled\鸢尾花\iris.data', dtype=float, delimiter=',', converters={4:iris_type})

'''

def loadtxt(fname, dtype=float, comments='#', delimiter=None,

converters=None, skiprows=0, usecols=None, unpack=False,

ndmin=0, encoding='bytes', max_rows=None):

'''

x, y = np.split(data, (4, ), axis=1)

x_train, x_test, y_train, y_test = mo.train_test_split(x, y, random_state=1, test_size=0.3)

'''

train_data：被划分的样本特征集

train_target：被划分的样本标签

test_size：如果是浮点数，在0-1之间，表示样本占比；如果是整数的话就是样本的数量

random_state：是随机数的种子。

随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机