Python sparse.rand方法代碼示例

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def testVStackExecution(self):
        a_data = np.random.rand(10)
        b_data = np.random.rand(10)

        a = tensor(a_data, chunk_size=4)
        b = tensor(b_data, chunk_size=4)

        c = vstack([a, b])
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.vstack([a_data, b_data])
        self.assertTrue(np.array_equal(res, expected))

        a_data = np.random.rand(10, 20)
        b_data = np.random.rand(5, 20)

        a = tensor(a_data, chunk_size=3)
        b = tensor(b_data, chunk_size=4)

        c = vstack([a, b])
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.vstack([a_data, b_data])
        self.assertTrue(np.array_equal(res, expected)) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def testDStackExecution(self):
        a_data = np.random.rand(10)
        b_data = np.random.rand(10)

        a = tensor(a_data, chunk_size=4)
        b = tensor(b_data, chunk_size=4)

        c = dstack([a, b])
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.dstack([a_data, b_data])
        self.assertTrue(np.array_equal(res, expected))

        a_data = np.random.rand(10, 20)
        b_data = np.random.rand(10, 20)

        a = tensor(a_data, chunk_size=3)
        b = tensor(b_data, chunk_size=4)

        c = dstack([a, b])
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.dstack([a_data, b_data])
        self.assertTrue(np.array_equal(res, expected)) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def testColumnStackExecution(self):
        a_data = np.array((1, 2, 3))
        b_data = np.array((2, 3, 4))
        a = tensor(a_data, chunk_size=1)
        b = tensor(b_data, chunk_size=2)

        c = column_stack((a, b))
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.column_stack((a_data, b_data))
        np.testing.assert_equal(res, expected)

        a_data = np.random.rand(4, 2, 3)
        b_data = np.random.rand(4, 2, 3)
        a = tensor(a_data, chunk_size=1)
        b = tensor(b_data, chunk_size=2)

        c = column_stack((a, b))
        res = self.executor.execute_tensor(c, concat=True)[0]
        expected = np.column_stack((a_data, b_data))
        np.testing.assert_equal(res, expected) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_linear_regression_sparse_equal_dense(normalize, fit_intercept):
    # Test that linear regression agrees between sparse and dense
    rng = check_random_state(0)
    n_samples = 200
    n_features = 2
    X = rng.randn(n_samples, n_features)
    X[X < 0.1] = 0.
    Xcsr = sparse.csr_matrix(X)
    y = rng.rand(n_samples)
    params = dict(normalize=normalize, fit_intercept=fit_intercept)
    clf_dense = LinearRegression(**params)
    clf_sparse = LinearRegression(**params)
    clf_dense.fit(X, y)
    clf_sparse.fit(Xcsr, y)
    assert clf_dense.intercept_ == pytest.approx(clf_sparse.intercept_)
    assert_allclose(clf_dense.coef_, clf_sparse.coef_) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_robust_scaler_equivalence_dense_sparse(density, strictly_signed):
    # Check the equivalence of the fitting with dense and sparse matrices
    X_sparse = sparse.rand(1000, 5, density=density).tocsc()
    if strictly_signed == 'positive':
        X_sparse.data = np.abs(X_sparse.data)
    elif strictly_signed == 'negative':
        X_sparse.data = - np.abs(X_sparse.data)
    elif strictly_signed == 'zeros':
        X_sparse.data = np.zeros(X_sparse.data.shape, dtype=np.float64)
    X_dense = X_sparse.toarray()

    scaler_sparse = RobustScaler(with_centering=False)
    scaler_dense = RobustScaler(with_centering=False)

    scaler_sparse.fit(X_sparse)
    scaler_dense.fit(X_dense)

    assert_allclose(scaler_sparse.scale_, scaler_dense.scale_) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_solve(self):
        # Test whether the lu_solve command segfaults, as reported by Nils
        # Wagner for a 64-bit machine, 02 March 2005 (EJS)
        n = 20
        np.random.seed(0)  # make tests repeatable
        A = zeros((n,n), dtype=complex)
        x = np.random.rand(n)
        y = np.random.rand(n-1)+1j*np.random.rand(n-1)
        r = np.random.rand(n)
        for i in range(len(x)):
            A[i,i] = x[i]
        for i in range(len(y)):
            A[i,i+1] = y[i]
            A[i+1,i] = conjugate(y[i])
        A = self.spmatrix(A)
        with suppress_warnings() as sup:
            sup.filter(SparseEfficiencyWarning, "splu requires CSC matrix format")
            x = splu(A).solve(r)
        assert_almost_equal(A*x,r) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_fancy_indexing_randomized(self):
        np.random.seed(1234)  # make runs repeatable

        NUM_SAMPLES = 50
        M = 6
        N = 4

        D = np.asmatrix(np.random.rand(M,N))
        D = np.multiply(D, D > 0.5)

        I = np.random.randint(-M + 1, M, size=NUM_SAMPLES)
        J = np.random.randint(-N + 1, N, size=NUM_SAMPLES)

        S = self.spmatrix(D)

        SIJ = S[I,J]
        if isspmatrix(SIJ):
            SIJ = SIJ.todense()
        assert_equal(SIJ, D[I,J])

        I_bad = I + M
        J_bad = J - N

        assert_raises(IndexError, S.__getitem__, (I_bad,J))
        assert_raises(IndexError, S.__getitem__, (I,J_bad)) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_arnoldi(self):
        np.random.rand(1234)

        A = eye(10000) + rand(10000,10000,density=1e-4)
        b = np.random.rand(10000)

        # The inner arnoldi should be equivalent to gmres
        with suppress_warnings() as sup:
            sup.filter(DeprecationWarning, ".*called without specifying.*")
            x0, flag0 = gcrotmk(A, b, x0=zeros(A.shape[0]), m=15, k=0, maxiter=1)
            x1, flag1 = gmres(A, b, x0=zeros(A.shape[0]), restart=15, maxiter=1)

        assert_equal(flag0, 1)
        assert_equal(flag1, 1)
        assert_(np.linalg.norm(A.dot(x0) - b) > 1e-3)

        assert_allclose(x0, x1) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_arnoldi(self):
        np.random.rand(1234)

        A = eye(10000) + rand(10000,10000,density=1e-4)
        b = np.random.rand(10000)

        # The inner arnoldi should be equivalent to gmres
        with suppress_warnings() as sup:
            sup.filter(DeprecationWarning, ".*called without specifying.*")
            x0, flag0 = lgmres(A, b, x0=zeros(A.shape[0]), inner_m=15, maxiter=1)
            x1, flag1 = gmres(A, b, x0=zeros(A.shape[0]), restart=15, maxiter=1)

        assert_equal(flag0, 1)
        assert_equal(flag1, 1)
        assert_(np.linalg.norm(A.dot(x0) - b) > 1e-3)

        assert_allclose(x0, x1) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_conversion_with_sparse_X(self):
        """Tests conversion of a model that's fitted with sparse data."""
        num_samples = 100
        num_dims = 64
        sparse_X = sparse.rand(
            num_samples, num_dims, format="csr"
        )  # KNeighborsClassifier only supports CSR format
        y = self.iris_y[
            0:num_samples
        ]  # the labels themselves don't matter - just use 100 of the Iris ones

        sklearn_model = KNeighborsClassifier(algorithm="brute")
        sklearn_model.fit(sparse_X, y)

        coreml_model = sklearn.convert(sklearn_model)
        coreml_spec = coreml_model.get_spec()
        self.assertIsNotNone(coreml_spec) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_simulate_glm(distr):
    """Test that every generative model can be simulated from."""

    random_state = 1
    state = np.random.RandomState(random_state)
    n_samples, n_features = 10, 3

    # sample random coefficients
    beta0 = state.rand()
    beta = state.normal(0.0, 1.0, n_features)

    X = state.normal(0.0, 1.0, [n_samples, n_features])
    simulate_glm(distr, beta0, beta, X, random_state=random_state)

    with pytest.raises(ValueError, match="'beta0' must be float"):
        simulate_glm(distr, np.array([1.0]), beta, X, random_state)

    with pytest.raises(ValueError, match="'beta' must be 1D"):
        simulate_glm(distr, 1.0, np.atleast_2d(beta), X, random_state)

    # If the distribution name is garbage it will fail
    distr = 'multivariate_gaussian_poisson'
    with pytest.raises(ValueError, match="'distr' must be in"):
        simulate_glm(distr, 1.0, 1.0, np.array([[1.0]])) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def generate_dummy_data(num_users=15000, num_items=30000, interaction_density=.00045, num_user_features=200,
                        num_item_features=200, n_features_per_user=20, n_features_per_item=20,  pos_int_ratio=.5,
                        return_datasets=False):

    if pos_int_ratio <= 0.0:
        raise Exception("pos_int_ratio must be > 0")

    print("Generating positive interactions")
    interactions = sp.rand(num_users, num_items, density=interaction_density * pos_int_ratio)
    if pos_int_ratio < 1.0:
        print("Generating negative interactions")
        interactions += -1 * sp.rand(num_users, num_items, density=interaction_density * (1 - pos_int_ratio))

    print("Generating user features")
    user_features = sp.rand(num_users, num_user_features, density=float(n_features_per_user) / num_user_features)

    print("Generating item features")
    item_features = sp.rand(num_items, num_item_features, density=float(n_features_per_item) / num_item_features)

    if return_datasets:
        interactions = create_tensorrec_dataset_from_sparse_matrix(interactions)
        user_features = create_tensorrec_dataset_from_sparse_matrix(user_features)
        item_features = create_tensorrec_dataset_from_sparse_matrix(item_features)

    return interactions, user_features, item_features 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def test_overflow_predict():

    no_users, no_items = (1000, 1000)

    train = sp.rand(no_users, no_items, format="csr", random_state=42)

    model = LightFM(loss="warp")

    model.fit(train)

    with pytest.raises((ValueError, OverflowError)):
        print(
            model.predict(
                1231241241231241414,
                np.arange(no_items),
                user_features=sp.identity(no_users),
            )
        ) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def __init__(self,ndims=36,nbasis=72,nbatch=100,logalpha=None,W=None,b=None):
        """ Product of T experts, assumes a fixed W that is sparse and alpha that is
        """
        self.ndims=ndims
        self.nbasis=nbasis
        self.nbatch=nbatch
        if W is  None:
           rand_val = rand(ndims,nbasis/2,density=0.25)
           W = np.concatenate([rand_val.toarray(), -rand_val.toarray()],axis=1)
        self.W = theano.shared(np.array(W,dtype='float32'),'W')
        if logalpha is None:
            logalpha = np.random.randn(nbasis,)
        self.logalpha = theano.shared(np.array(logalpha,dtype='float32'),'alpha')
        if b is None:
            b = np.zeros((nbasis,))
        self.b = theano.shared(np.array(b,dtype='float32'),'b')
        X = T.matrix()
        E = self.E_def(X)
        dEdX = T.grad(T.sum(E),X)
        #@overrides(Distribution)
        self.E_val=theano.function([X],E,allow_input_downcast=True)
        #@overrides(Distribution)
        self.dEdX_val = theano.function([X],dEdX,allow_input_downcast=True)
        super(ProductOfT,self).__init__(ndims,nbatch) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import rand [as 別名]
def _get_uniform_dataset_csr(num_rows, num_cols, density=0.1, dtype=None,
                             data_init=None, shuffle_csr_indices=False):
    """Returns CSRNDArray with uniform distribution
    This generates a csr matrix with totalnnz unique randomly chosen numbers
    from num_rows*num_cols and arranges them in the 2d array in the
    following way:
    row_index = (random_number_generated / num_rows)
    col_index = random_number_generated - row_index * num_cols
    """
    _validate_csr_generation_inputs(num_rows, num_cols, density,
                                    distribution="uniform")
    try:
        from scipy import sparse as spsp
        csr = spsp.rand(num_rows, num_cols, density, dtype=dtype, format="csr")
        if data_init is not None:
            csr.data.fill(data_init)
        if shuffle_csr_indices is True:
            shuffle_csr_column_indices(csr)
        result = mx.nd.sparse.csr_matrix((csr.data, csr.indices, csr.indptr),
                                         shape=(num_rows, num_cols), dtype=dtype)
    except ImportError:
        assert(data_init is None), \
               "data_init option is not supported when scipy is absent"
        assert(not shuffle_csr_indices), \
               "shuffle_csr_indices option is not supported when scipy is absent"
        # scipy not available. try to generate one from a dense array
        dns = mx.nd.random.uniform(shape=(num_rows, num_cols), dtype=dtype)
        masked_dns = dns * (dns < density)
        result = masked_dns.tostype('csr')
    return result