def test_experimental(self):
recognizer = Recognizer()
recognizer.load_data([
'BASE',
'FLOWER',
'JAIL',
'SNOW',
'STRIPE_1',
'STRIPE_2',
'STRIPE_3',
'TRI',
])
recognizer.model = [
RecognizerFlatten(),
RecognizerSplit(),
RecognizerPCA(),
RecognizerLDA(),
RecognizerSVM()
]
recognizer.model[1].n_components = 80
recognizer.model[2].n_components = 10
recognizer.fit()
recognizer.model[1].n_components = 75
recognizer.model[2].n_components = 30
self.assertGreater(recognizer.score(), 0.3)
def test_experimental(self):
recognizer = Recognizer()
recognizer.load_data([
# 'BLACK', 'BLUE', 'BROWN', 'GREEN',
# 'PINK', 'WHITE', 'YELLOW',
# 'CONE', 'MAPSCROLL', 'STONE',
# 'NA',
'BASE',
'FLOWER', 'JAIL', 'SNOW',
'STRIPE_1', 'STRIPE_2', 'STRIPE_3',
'TRI',
])
recognizer.model = [RecognizerFlatten(), RecognizerSplit(), RecognizerPCA(), RecognizerLDA(), RecognizerSVM()]
result = []
# for n_pca in [10, 50, 80, 100, 150, 200, 300, 500, 700]:
for n_pca in [150]:
# for n_lda in [1, 3, 5, 7, 10, 15, 30, 50, 70, 100, 130, 200, 250, 300]:
for n_lda in [15]:
recognizer.model[1].n_components = n_pca
recognizer.model[2].n_components = n_lda
n = 10
score = []
for i in range(n):
recognizer.fit()
score.append(recognizer.score())
avg = float(sum(score)) / n
import sys
print {'n_pca': n_pca, 'n_lda': n_lda, 'score': avg}
result.append({'n_pca': n_pca, 'n_lda': n_lda, 'score': avg})
import sys
print result
recognizer.model[1].n_components = 75
recognizer.model[2].n_components = 30
self.assertGreater(recognizer.score(), 0.5)
def test_fit_predict_with_Split_PCA_SVM(self):
self.subject.load_data(['BLACK', 'BLUE'])
self.subject.model = [
RecognizerFlatten(),
RecognizerSplit(),
RecognizerPCA(),
RecognizerSVM()
]
self.subject.fit()
self.subject.score()
def test_fit_predict_with_PCA_SVM(self):
self.subject.load_data(['BLACK', 'BLUE'])
self.subject.model = [
RecognizerFlatten(),
RecognizerPCA(),
RecognizerSVM()
]
self.subject.fit()
for X, y in zip(self.subject.data.X, self.subject.data.y):
self.assertEqual(self.subject.predict(X), y)
def test_dump_load(self):
model_filename = 'tmp/test_model.pkl'
self.subject.load_data(['BLACK', 'BLUE'])
self.subject.model = [
RecognizerFlatten(),
RecognizerPCA(),
RecognizerSVM()
]
self.subject.fit()
self.subject.dump(model_filename)
self.subject = Recognizer()
self.subject.load(model_filename)
data = Data()
data.load_data(['BLACK', 'BLUE'])
for X, y in zip(data.X, data.y):
self.assertEqual(self.subject.predict(X), y)
def test_basic_usage(self):
model_filename = 'tmp/basic_usage.pkl'
recognizer = Recognizer()
recognizer.load_data([
'BLACK',
'BLUE',
'BROWN',
'GREEN',
'PINK',
'WHITE',
'YELLOW',
'CONE',
'MAPSCROLL',
'STONE',
'NA',
])
recognizer.model = [
RecognizerFlatten(),
RecognizerSplit(),
RecognizerPCA(),
RecognizerSVM()
]
recognizer.fit()
self.assertGreater(recognizer.score(), 0.9)
recognizer.clear()
recognizer.dump(model_filename)
recognizer = Recognizer()
recognizer.load(model_filename)
data = Data()
data.load_data([
'BLACK',
'BLUE',
'BROWN',
'GREEN',
'PINK',
'WHITE',
'YELLOW',
'CONE',
'MAPSCROLL',
'STONE',
'NA',
])
self.assertGreater(recognizer.score(data), 0.97)
def test_fit_predict(self):
self.subject.load_data(['BLACK', 'BLUE'])
self.subject.model = [RecognizerFlatten(), RecognizerPCA()]
self.subject.fit()
for X, y in zip(self.subject.data.X, self.subject.data.y):
self.assertTrue(self.subject.predict(X) is not None)