Python MultiLayerPerceptron._predict 예제들

예제 #1

파일: test_types.py 프로젝트: Sandy4321/scikit-neuralnetwork

class TestScipySparseMatrix(unittest.TestCase):

    def setUp(self):
        self.nn = MLP(layers=[L("Gaussian", units=4)], n_iter=1)

    def test_FitFloat64(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float64)
            y = sparse_matrix((8, 4), dtype=numpy.float64)
            self.nn._fit(X, y)

    def test_FitFloat32(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            y = sparse_matrix((8, 4), dtype=numpy.float32)
            self.nn._fit(X, y)

    def test_Predict64(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float64)
            self.nn._predict(X)

    def test_Predict32(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            self.nn._predict(X)

예제 #2

class TestScipySparseMatrix(unittest.TestCase):
    def setUp(self):
        self.nn = MLP(layers=[L("Linear", units=4)], n_iter=1)

    def test_FitFloat64(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float64)
            y = sparse_matrix((8, 4), dtype=numpy.float64)
            self.nn._fit(X, y)

    def test_FitFloat32(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            y = sparse_matrix((8, 4), dtype=numpy.float32)
            self.nn._fit(X, y)

    def test_FitHybrid(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            y = numpy.zeros((8, 4), dtype=numpy.float32)
            self.nn._fit(X, y)

    def test_FitMutator(self):
        def mutate(x):
            x -= 0.5
            self.count += 1

        self.nn.mutator = mutate

        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            y = numpy.zeros((8, 4), dtype=numpy.float32)

            self.count = 0
            assert_equal(0, self.count)
            self.nn._fit(X, y)
            assert_equal(8, self.count)

    def test_Predict64(self):
        theano.config.floatX = 'float64'
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float64)
            yp = self.nn._predict(X)
            assert_equal(yp.dtype, numpy.float64)

    def test_Predict32(self):
        theano.config.floatX = 'float32'
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            yp = self.nn._predict(X)
            assert_equal(yp.dtype, numpy.float32)

예제 #3

파일: test_types.py 프로젝트: mangwang/scikit-neuralnetwork

class TestScipySparseMatrix(unittest.TestCase):

    def setUp(self):
        self.nn = MLP(layers=[L("Linear", units=4)], n_iter=1)

    def test_FitFloat64(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float64)
            y = sparse_matrix((8, 4), dtype=numpy.float64)
            self.nn._fit(X, y)

    def test_FitFloat32(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            y = sparse_matrix((8, 4), dtype=numpy.float32)
            self.nn._fit(X, y)

    def test_FitHybrid(self):
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            y = numpy.zeros((8, 4), dtype=numpy.float32)
            self.nn._fit(X, y)

    def test_FitMutator(self):
        def mutate(x):
            self.count += 1
            return x - 0.5
        self.nn.mutator = mutate

        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            y = numpy.zeros((8, 4), dtype=numpy.float32)

            self.count = 0
            assert_equal(0, self.count)
            self.nn._fit(X, y)
            assert_equal(8, self.count)

    def test_Predict64(self):
        theano.config.floatX = 'float64'
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float64)
            yp = self.nn._predict(X)
            assert_equal(yp.dtype, numpy.float64)

    def test_Predict32(self):
        theano.config.floatX = 'float32'
        for t in SPARSE_TYPES:
            sparse_matrix = getattr(scipy.sparse, t)
            X = sparse_matrix((8, 4), dtype=numpy.float32)
            yp = self.nn._predict(X)
            assert_equal(yp.dtype, numpy.float32)

예제 #4

class TestInputDataTypes(unittest.TestCase):
    def setUp(self):
        self.nn = MLP(layers=[L("Gaussian")], n_iter=1)

    def test_FitSciPySparse(self):
        X, y = scipy.sparse.csr_matrix((8, 4)), scipy.sparse.csr_matrix((8, 4))
        self.nn._fit(X, y)

    def test_PredictSciPySparse(self):
        X, y = scipy.sparse.csr_matrix((8, 4)), scipy.sparse.csr_matrix((8, 4))
        self.nn._fit(X, y)
        self.nn._predict(X)

예제 #5

파일: test_types.py 프로젝트: mangwang/scikit-neuralnetwork

    def test_TrainConstantOneEpoch(self):
        for t in ['csr_matrix', 'csc_matrix']:
            sparse_matrix = getattr(scipy.sparse, t)
            X_s, y_s = sparse_matrix((8, 16), dtype=numpy.float32), sparse_matrix((8, 16), dtype=numpy.float32)
            X, y = X_s.toarray(), y_s.toarray()
            
            nn1 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn1._fit(X, y)

            nn2 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn2._fit(X_s, y_s)

            assert_true(numpy.all(nn1._predict(X_s) == nn1._predict(X_s)))

예제 #6

파일: test_types.py 프로젝트: Sandy4321/scikit-neuralnetwork

    def test_TrainConstantOneEpoch(self):
        for t in ['csr_matrix', 'csc_matrix']:
            sparse_matrix = getattr(scipy.sparse, t)
            X_s, y_s = sparse_matrix((8, 16), dtype=numpy.float32), sparse_matrix((8, 16), dtype=numpy.float32)
            X, y = X_s.toarray(), y_s.toarray()
            
            nn1 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn1._fit(X, y)

            nn2 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn2._fit(X_s, y_s)

            assert_true(numpy.all(nn1._predict(X_s) == nn1._predict(X_s)))

예제 #7

파일: test_types.py 프로젝트: LEONOB2014/scikit-neuralnetwork

class TestInputDataTypes(unittest.TestCase):

    def setUp(self):
        self.nn = MLP(layers=[L("Gaussian")], n_iter=1)

    def test_FitSciPySparse(self):
        X, y = scipy.sparse.csr_matrix((8, 4)), scipy.sparse.csr_matrix((8, 4))
        self.nn._fit(X, y)

    def test_PredictSciPySparse(self):
        X, y = scipy.sparse.csr_matrix((8, 4)), scipy.sparse.csr_matrix((8, 4))
        self.nn._fit(X, y)
        self.nn._predict(X)

예제 #8

파일: test_types.py 프로젝트: mangwang/scikit-neuralnetwork

    def test_TrainRandomOneEpoch(self):
        for t in ['dok_matrix', 'lil_matrix']:
            sparse_matrix = getattr(scipy.sparse, t)
            X_s, y_s = sparse_matrix((8, 16), dtype=numpy.float32), sparse_matrix((8, 16), dtype=numpy.float32)
            for i in range(X_s.shape[0]):
                X_s[i,random.randint(0, X_s.shape[1]-1)] = 1.0
                y_s[i,random.randint(0, y_s.shape[1]-1)] = 1.0
            X, y = X_s.toarray(), y_s.toarray()

            nn1 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn1._fit(X, y)

            nn2 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn2._fit(X_s, y_s)

            assert_true(numpy.all(nn1._predict(X_s) == nn1._predict(X_s)))

예제 #9

파일: test_types.py 프로젝트: BK-University/scikit-neuralnetwork

class TestMemoryMap(unittest.TestCase):

    __types__ = ['float32', 'float64']

    def setUp(self):
        self.nn = MLP(layers=[L("Linear", units=3)], n_iter=1)
        self.directory = tempfile.mkdtemp()

    def tearDown(self):
        shutil.rmtree(self.directory)

    def make(self, name, shape, dtype):
        filename = os.path.join(self.directory, name)
        return numpy.memmap(filename, dtype=dtype, mode='w+', shape=shape)

    def test_FitAllTypes(self):
        for t in self.__types__:
            theano.config.floatX = t
            X = self.make('X', (12, 3), dtype=t)
            y = self.make('y', (12, 3), dtype=t)
            self.nn._fit(X, y)

    def test_PredictAllTypes(self):
        for t in self.__types__:
            theano.config.floatX = t
            X = self.make('X', (12, 3), dtype=t)
            yp = self.nn._predict(X)

예제 #10

파일: test_types.py 프로젝트: Sandy4321/scikit-neuralnetwork

    def test_TrainRandomOneEpoch(self):
        for t in ['dok_matrix', 'lil_matrix']:
            sparse_matrix = getattr(scipy.sparse, t)
            X_s, y_s = sparse_matrix((8, 16), dtype=numpy.float32), sparse_matrix((8, 16), dtype=numpy.float32)
            for i in range(X_s.shape[0]):
                X_s[i,random.randint(0, X_s.shape[1]-1)] = 1.0
                y_s[i,random.randint(0, y_s.shape[1]-1)] = 1.0
            X, y = X_s.toarray(), y_s.toarray()

            nn1 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn1._fit(X, y)

            nn2 = MLP(layers=[L("Linear")], n_iter=1, random_state=1234)
            nn2._fit(X_s, y_s)

            assert_true(numpy.all(nn1._predict(X_s) == nn1._predict(X_s)))

예제 #11

파일: test_types.py 프로젝트: edmarola/RedesNeuronales

class TestMemoryMap(unittest.TestCase):

    __types__ = ['float32', 'float64']

    def setUp(self):
        self.nn = MLP(layers=[L("Linear", units=3)], n_iter=1)
        self.directory = tempfile.mkdtemp()

    def tearDown(self):
        shutil.rmtree(self.directory)

    def make(self, name, shape, dtype):
        filename = os.path.join(self.directory, name)
        return numpy.memmap(filename, dtype=dtype, mode='w+', shape=shape)

    def test_FitAllTypes(self):
        for t in self.__types__:
            theano.config.floatX = t
            X = self.make('X', (12, 3), dtype=t)
            y = self.make('y', (12, 3), dtype=t)
            self.nn._fit(X, y)

    def test_PredictAllTypes(self):
        for t in self.__types__:
            theano.config.floatX = t
            X = self.make('X', (12, 3), dtype=t)
            yp = self.nn._predict(X)