SVM.py
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'''
SVM Model.
@author: chunk
chunkplus@gmail.com
2014 Dec
'''
from mfeat import *
from mmodel import *
from common import *
import numpy as np
import csv
import json
import pickle
import cv2
from sklearn import svm
dict_Train = {}
dict_databuf = {}
dict_tagbuf = {}
dict_featbuf = {}
class ModelSVM(ModelBase):
def __init__(self):
ModelBase.__init__(self)
def _train_sk(self, X, Y):
lin_clf = svm.LinearSVC()
lin_clf.fit(X, Y)
with open('res/svm_sk.model', 'wb') as modelfile:
model = pickle.dump(lin_clf, modelfile)
self.model = lin_clf
return lin_clf
def _predict_sk(self, feat, model=None):
"""N.B. sklearn.svm.base.predict :
Perform classification on samples in X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
y_pred : array, shape = [n_samples]
Class labels for samples in X.
"""
if model is None:
if self.model != None:
model = self.model
else:
with open('res/svm_sk.model', 'rb') as modelfile:
model = pickle.load(modelfile)
return model.predict(feat)
def _test_sk(self, X, Y, model=None):
if model is None:
if self.model != None:
model = self.model
else:
with open('res/svm_sk.model', 'rb') as modelfile:
model = pickle.load(modelfile)
result_Y = np.array(self._predict_sk(X, model))
print result_Y == Y
return np.mean(Y == result_Y)
def _train_cv(self, X, Y):
svm_params = dict(kernel_type=cv2.SVM_LINEAR,
svm_type=cv2.SVM_C_SVC,
C=2.67, gamma=5.383)
X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)
timer = Timer()
timer.mark()
svm = cv2.SVM()
svm.train(X, Y, params=svm_params)
svm.save('res/svm_cv.model')
self.model = svm
return svm
def _predict_cv(self, feat, model=None):
if model is None:
if self.model != None:
model = self.model
else:
with open('res/svm_cv.model', 'rb') as modelfile:
model = pickle.load(modelfile)
feat = np.array(feat, dtype=np.float32)
return model.predict(feat)
def _test_cv(self, X, Y, model=None):
if model is None:
if self.model != None:
model = self.model
else:
with open('res/svm_cv.model', 'rb') as modelfile:
model = pickle.load(modelfile)
X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)
# result_Y = np.array([self._predict_cv(x, model) for x in X])
result_Y = np.array(model.predict_all(X)).ravel()
return np.mean(Y == result_Y)
def train(self, X, Y):
return self._train_cv(X, Y)
def predict(self, feat, model=None):
return self._predict_cv(feat, model)
def test(self, X, Y, model=None):
return self._test_cv(X, Y, model)