眼底血管分割训练函数（SVM，Adaboost）

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

import numpy as np
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import zero_one_loss
from sklearn.ensemble import AdaBoostClassifier

path = u'extract.txt'

data0 = np.loadtxt(path,dtype = str,delimiter = ' ')
data = np.array( [[row[i] for i in range(0, 8) if i != 7] for row in data0] )#del the last col of the array
x = data[:,(1,2,3,4,5,6)]
y = data[:,0]
x = np.uint8(x)
y = np.uint8(y)
#y[y[:]==255]=1
#y[y[:]==0]=-1
#x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.6)
#clf = svm.SVC()
"""
n_estimators = 800
learning_rate = 1.0
dt_stump = DecisionTreeClassifier(max_depth=12, min_samples_leaf=1)
dt_stump.fit(x, y)
ada_discrete = AdaBoostClassifier(
    base_estimator=dt_stump,
    learning_rate=learning_rate,
    n_estimators=n_estimators,
    algorithm="SAMME")
ada_discrete.fit(x, y)
"""
clf = svm.SVC(C=0.8, kernel='rbf', gamma=20, decision_function_shape='ovr')
clf.fit(x, y.ravel())
joblib.dump(clf, "train_model.m")