#_*_ coding:utf-8 _*_

from sklearn import datasets

from sklearn import svm

#装载内部测试数据集

digits = datasets.load_digits()

#设置参数

clf = svm.SVC(gamma = 0.001,C = 100.)

#训练

clf.fit(digits.data[:-1],digits.target[:-1])

#预测

print clf.predict(digits.data[-1:])

#_*_ coding:utf-8 _*_

from sklearn import datasets

from sklearn import svm

import pickle

from sklearn.externals import joblib

#装载内部测试数据集

iris = datasets.load_iris()

X,y = iris.data,iris.target

#初始化模型

clf = svm.SVC()

#训练

clf.fit(X[:-1],y[:-1])

#保存模型

s = pickle.dumps(clf)

#装载模型

clf2 = pickle.loads(s)

#预测

print clf2.predict(X[-1:])

#_*_ coding:utf-8 _*_

from sklearn import datasets

from sklearn import svm

from sklearn.externals import joblib

#装载内部测试数据集

iris = datasets.load_iris()

X,y = iris.data,iris.target

#初始化模型

clf = svm.SVC()

#训练

clf.fit(X[:-1],y[:-1])

#保存模型

joblib.dump(clf,'filename.pkl')

#装载模型

clf2 = joblib.load('filename.pkl')

#预测

print clf2.predict(X[-1:])

#_*_ coding:utf-8 _*_

import numpy as np

from sklearn import random_projection

rng = np.random.RandomState(0)

X = rng.rand(10,2000)

Y = np.array(X)

X = np.array(X,dtype='float32')

print Y.dtype,X.dtype

transformer = random_projection.GaussianRandomProjection()

X_new = transformer.fit_transform(X)

print X_new.dtype

#_*_ coding:utf-8 _*_

import numpy as np

from sklearn.svm import SVC

rng = np.random.RandomState(0)

X = rng.rand(100,10)

y = rng.binomial(1,0.5,100)

X_test = rng.rand(5,10)

clf = SVC()

clf.set_params(kernel = 'linear').fit(X,y)

print clf.predict(X_test)

clf.set_params(kernel = 'rbf').fit(X,y)

print clf.predict(X_test)