python类inner()的实例源码

core.py 文件源码 项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda 作者: SignalMedia 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def inner(a, b):
    """
    Returns the inner product of a and b for arrays of floating point types.

    Like the generic NumPy equivalent the product sum is over the last dimension
    of a and b.

    Notes
    -----
    The first argument is not conjugated.

    """
    fa = filled(a, 0)
    fb = filled(b, 0)
    if len(fa.shape) == 0:
        fa.shape = (1,)
    if len(fb.shape) == 0:
        fb.shape = (1,)
    return np.inner(fa, fb).view(MaskedArray)
lowd_meek.py 文件源码 项目:Steal-ML 作者: ftramer 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def find_starters(self):
        """
        This function finds a pair of instances. One positive and one negative
        :param clf: classifier being extracted
        :return: (x+, x-) a pair of instances
        """
        # perdict = 1 ? inner(x, coef) + intercept_ > 0 : 0

        x_n, x_p = (None, None)
        x_n_found = False
        x_p_found = False
        for d in self.X_test:
            if x_n_found and x_p_found:
                break

            if self.query(d) == 1 and (not x_p_found):
                x_p = d
                x_p_found = True
            elif self.query(d) == self.NEG and (not x_n_found):
                x_n = d
                x_n_found = True
        return x_p, x_n
smearing.py 文件源码 项目:upho 作者: yuzie007 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def run(self, peaks, weights=None):
        """Get smeared values.

        Args:
            peaks:
            weights:
                Weight factors for "peaks".
                Now this can be one-dimeansional and multi-dimensional arrays.
                The last dimension must have the same order as the "peaks".
        """
        smearing_function = self._smearing_function
        xs = self._xs
        sigma = self._sigma

        tmp = smearing_function(xs[:, None], peaks[None, :], sigma)
        if weights is not None:
            values = np.inner(tmp, weights)
        else:
            values = np.sum(tmp, axis=1)

        return values
eigenstates.py 文件源码 项目:upho 作者: yuzie007 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def _create_rotational_weights_for_elements(self, kpoint, transformation_matrix, vectors):
        """

        Parameters
        ----------
        kpoint : 1d array
            Reciprocal space point in fractional coordinates for PC.
        vectors : (..., natoms_p * ndims, nbands) array
            Vectors for SC after translational projection.
        """
        projected_vectors = self._rotational_projector.project_vectors(
            vectors, kpoint, transformation_matrix)

        nirreps, natoms_p, nelms, tmp, nbands = projected_vectors.shape

        shape = (nirreps, natoms_p, nelms, natoms_p, nelms, nbands)
        weights = np.zeros(shape, dtype=complex)
        for i in range(nirreps):
            for j in range(nbands):
                weights[i, ..., j] = np.inner(
                    np.conj(projected_vectors[i, ..., j]), projected_vectors[i, ..., j])

        return weights, projected_vectors
test_einsum.py 文件源码 项目:aws-lambda-numpy 作者: vitolimandibhrata 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def test_einsum_misc(self):
        # This call used to crash because of a bug in
        # PyArray_AssignZero
        a = np.ones((1, 2))
        b = np.ones((2, 2, 1))
        assert_equal(np.einsum('ij...,j...->i...', a, b), [[[2], [2]]])

        # The iterator had an issue with buffering this reduction
        a = np.ones((5, 12, 4, 2, 3), np.int64)
        b = np.ones((5, 12, 11), np.int64)
        assert_equal(np.einsum('ijklm,ijn,ijn->', a, b, b),
                        np.einsum('ijklm,ijn->', a, b))

        # Issue #2027, was a problem in the contiguous 3-argument
        # inner loop implementation
        a = np.arange(1, 3)
        b = np.arange(1, 5).reshape(2, 2)
        c = np.arange(1, 9).reshape(4, 2)
        assert_equal(np.einsum('x,yx,zx->xzy', a, b, c),
                    [[[1,  3], [3,  9], [5, 15], [7, 21]],
                    [[8, 16], [16, 32], [24, 48], [32, 64]]])
test_core.py 文件源码 项目:aws-lambda-numpy 作者: vitolimandibhrata 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def test_TakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
        assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
        assert_equal(np.inner(filled(x, 0), filled(y, 0)),
                     inner(x, y))
        assert_equal(np.outer(filled(x, 0), filled(y, 0)),
                     outer(x, y))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)
test_old_ma.py 文件源码 项目:aws-lambda-numpy 作者: vitolimandibhrata 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def test_testTakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
                   inner(x, y)))
        assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
                   outer(x, y)))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)
timer_comparison.py 文件源码 项目:aws-lambda-numpy 作者: vitolimandibhrata 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def test_4(self):
        """
        Test of take, transpose, inner, outer products.

        """
        x = self.arange(24)
        y = np.arange(24)
        x[5:6] = self.masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
                            self.inner(x, y))
        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
                            self.outer(x, y))
        y = self.array(['abc', 1, 'def', 2, 3], object)
        y[2] = self.masked
        t = self.take(y, [0, 3, 4])
        assert t[0] == 'abc'
        assert t[1] == 2
        assert t[2] == 3
core.py 文件源码 项目:aws-lambda-numpy 作者: vitolimandibhrata 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def inner(a, b):
    """
    Returns the inner product of a and b for arrays of floating point types.

    Like the generic NumPy equivalent the product sum is over the last dimension
    of a and b.

    Notes
    -----
    The first argument is not conjugated.

    """
    fa = filled(a, 0)
    fb = filled(b, 0)
    if len(fa.shape) == 0:
        fa.shape = (1,)
    if len(fb.shape) == 0:
        fb.shape = (1,)
    return np.inner(fa, fb).view(MaskedArray)
name.py 文件源码 项目:color-extractor 作者: algolia 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def _hard_monochrome(self, sample):
        """
        Return the monochrome colors corresponding to `sample`, if any.
        A boolean is also returned, specifying whether or not the saturation is
        sufficient for non monochrome colors.
        """
        gray_proj = np.inner(sample, Name._GRAY_UNIT) * Name._GRAY_UNIT
        gray_dist = norm(sample - gray_proj)

        if gray_dist > 15:
            return []

        colors = []
        luminance = np.sum(sample * Name._GRAY_COEFF)
        if luminance > 45 and luminance < 170:
            colors.append(self._settings['gray_name'])
        if luminance <= 50:
            colors.append(self._settings['black_name'])
        if luminance >= 170:
            colors.append(self._settings['white_name'])

        return colors

    # Normalized identity (BGR gray) vector.
test_einsum.py 文件源码 项目:lambda-numba 作者: rlhotovy 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def test_einsum_misc(self):
        # This call used to crash because of a bug in
        # PyArray_AssignZero
        a = np.ones((1, 2))
        b = np.ones((2, 2, 1))
        assert_equal(np.einsum('ij...,j...->i...', a, b), [[[2], [2]]])

        # The iterator had an issue with buffering this reduction
        a = np.ones((5, 12, 4, 2, 3), np.int64)
        b = np.ones((5, 12, 11), np.int64)
        assert_equal(np.einsum('ijklm,ijn,ijn->', a, b, b),
                        np.einsum('ijklm,ijn->', a, b))

        # Issue #2027, was a problem in the contiguous 3-argument
        # inner loop implementation
        a = np.arange(1, 3)
        b = np.arange(1, 5).reshape(2, 2)
        c = np.arange(1, 9).reshape(4, 2)
        assert_equal(np.einsum('x,yx,zx->xzy', a, b, c),
                    [[[1,  3], [3,  9], [5, 15], [7, 21]],
                    [[8, 16], [16, 32], [24, 48], [32, 64]]])
test_einsum.py 文件源码 项目:lambda-numba 作者: rlhotovy 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def test_einsum_all_contig_non_contig_output(self):
        # Issue gh-5907, tests that the all contiguous special case
        # actually checks the contiguity of the output
        x = np.ones((5, 5))
        out = np.ones(10)[::2]
        correct_base = np.ones(10)
        correct_base[::2] = 5
        # Always worked (inner iteration is done with 0-stride):
        np.einsum('mi,mi,mi->m', x, x, x, out=out)
        assert_array_equal(out.base, correct_base)
        # Example 1:
        out = np.ones(10)[::2]
        np.einsum('im,im,im->m', x, x, x, out=out)
        assert_array_equal(out.base, correct_base)
        # Example 2, buffering causes x to be contiguous but
        # special cases do not catch the operation before:
        out = np.ones((2, 2, 2))[..., 0]
        correct_base = np.ones((2, 2, 2))
        correct_base[..., 0] = 2
        x = np.ones((2, 2), np.float32)
        np.einsum('ij,jk->ik', x, x, out=out)
        assert_array_equal(out.base, correct_base)
test_core.py 文件源码 项目:lambda-numba 作者: rlhotovy 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def test_TakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
        assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
        assert_equal(np.inner(filled(x, 0), filled(y, 0)),
                     inner(x, y))
        assert_equal(np.outer(filled(x, 0), filled(y, 0)),
                     outer(x, y))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)
timer_comparison.py 文件源码 项目:lambda-numba 作者: rlhotovy 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def test_4(self):
        """
        Test of take, transpose, inner, outer products.

        """
        x = self.arange(24)
        y = np.arange(24)
        x[5:6] = self.masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
                            self.inner(x, y))
        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
                            self.outer(x, y))
        y = self.array(['abc', 1, 'def', 2, 3], object)
        y[2] = self.masked
        t = self.take(y, [0, 3, 4])
        assert t[0] == 'abc'
        assert t[1] == 2
        assert t[2] == 3
core.py 文件源码 项目:lambda-numba 作者: rlhotovy 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def inner(a, b):
    """
    Returns the inner product of a and b for arrays of floating point types.

    Like the generic NumPy equivalent the product sum is over the last dimension
    of a and b.

    Notes
    -----
    The first argument is not conjugated.

    """
    fa = filled(a, 0)
    fb = filled(b, 0)
    if len(fa.shape) == 0:
        fa.shape = (1,)
    if len(fb.shape) == 0:
        fb.shape = (1,)
    return np.inner(fa, fb).view(MaskedArray)
solver.py 文件源码 项目:picard 作者: pierreablin 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def _solve_hessian(G, Y, thY, precon, lambda_min):
    N, T = Y.shape
    # Compute the derivative of the score
    psidY = ne.evaluate('(- thY ** 2 + 1.) / 2.')  # noqa
    # Build the diagonal of the Hessian, a.
    Y_squared = Y ** 2
    if precon == 2:
        a = np.inner(psidY, Y_squared) / float(T)
    elif precon == 1:
        sigma2 = np.mean(Y_squared, axis=1)
        psidY_mean = np.mean(psidY, axis=1)
        a = psidY_mean[:, None] * sigma2[None, :]
        diagonal_term = np.mean(Y_squared * psidY) + 1.
        a[np.diag_indices_from(a)] = diagonal_term
    else:
        raise ValueError('precon should be 1 or 2')
    # Compute the eigenvalues of the Hessian
    eigenvalues = 0.5 * (a + a.T - np.sqrt((a - a.T) ** 2 + 4.))
    # Regularize
    problematic_locs = eigenvalues < lambda_min
    np.fill_diagonal(problematic_locs, False)
    i_pb, j_pb = np.where(problematic_locs)
    a[i_pb, j_pb] += lambda_min - eigenvalues[i_pb, j_pb]
    # Invert the transform
    return (G * a.T - G.T) / (a * a.T - 1.)
test_solver.py 文件源码 项目:picard 作者: pierreablin 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def test_picard():
    N, T = 2, 10000
    rng = np.random.RandomState(42)
    S = rng.laplace(size=(N, T))
    A = rng.randn(N, N)
    X = np.dot(A, S)
    for precon in [1, 2]:
        Y, W = picard(X, precon=precon, verbose=True)
        # Get the final gradient norm
        G = np.inner(np.tanh(Y / 2.), Y) / float(T) - np.eye(N)
        assert_allclose(G, np.zeros((N, N)), atol=1e-7)
        assert_equal(Y.shape, X.shape)
        assert_equal(W.shape, A.shape)
        WA = np.dot(W, A)
        WA = get_perm(WA)[1]  # Permute and scale
        assert_allclose(WA, np.eye(N), rtol=1e-2, atol=1e-2)
test_solver.py 文件源码 项目:picard 作者: pierreablin 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def test_picardo():
    N, T = 2, 10000
    rng = np.random.RandomState(42)
    S = rng.laplace(size=(N, T))
    A = rng.randn(N, N)
    X = np.dot(A, S)

    Y, W = picardo(X, verbose=2)
    # Get the final gradient norm
    G = np.inner(np.tanh(Y), Y) / float(T) - np.eye(N)
    G = (G - G.T)  # take skew-symmetric part
    assert_allclose(G, np.zeros((N, N)), atol=1e-7)
    assert_equal(Y.shape, X.shape)
    assert_equal(W.shape, A.shape)
    WA = np.dot(W, A)
    WA = get_perm(WA)[1]  # Permute and scale
    assert_allclose(WA, np.eye(N), rtol=1e-2, atol=1e-2)
camera_simile.py 文件源码 项目:SIMILE 作者: hoangminhle 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def interpolate_learned_policy(old_policy, new_policy, interpolate, old_coeff, new_coeff, weight, method):
    if method is "stack_vel_pos":
        learned_trajectory = np.zeros(human.shape)
        for item in inPlay:
            for index in np.arange(item[0],item[0]+tao):
                learned_trajectory[index] = human[index]
            for index in np.arange(item[0]+tao,item[1]+1):
                feature = autoreg_game_context[index,:]
                for i in range(tao-1):
                    feature = np.append(feature, learned_trajectory[index-(i+1)] - learned_trajectory[index-(i+2)])
                for i in range(tao):
                    feature = np.append(feature,learned_trajectory[index-(i+1)])
                previous_prediction = learned_trajectory[index-tao:index].copy()
                previous_prediction = previous_prediction[::-1]
                old_model_predict = (old_policy.predict(feature) + np.inner(old_coeff, previous_prediction) * weight) / (1+weight)
                new_model_predict = (new_policy.predict(feature) + np.inner(new_coeff, previous_prediction) * weight) / (1+weight)
                #current_prediction = interpolate * new_policy.predict(feature) + (1-interpolate) * old_policy.predict(feature)
                learned_trajectory[index] = interpolate * new_model_predict + (1-interpolate) * old_model_predict
    return learned_trajectory
camera_simile.py 文件源码 项目:SIMILE 作者: hoangminhle 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def interpolate_test_policy(old_policy, new_policy, interpolate, reference_path, context, old_coeff, new_coeff, weight, method):
    Y_predict = np.zeros(reference_path.shape)
    if method is "stack_vel_pos":
        for i in range(len(reference_path)):
            if i<tao:
                Y_predict[i] = reference_path[i] #note: have the first tau frames correct
            else:
                feature = context[i]
                for j in range(tao-1):
                    feature = np.hstack((feature,Y_predict[i-(j+1)]-Y_predict[i-(j+2)]))
                for j in range(tao):
                    feature = np.hstack((feature,Y_predict[i-(j+1)]))
                previous_prediction = Y_predict[i-tao:i]
                previous_prediction = previous_prediction[::-1]
                #current_prediction = interpolate * new_policy.predict(feature) + (1-interpolate) * old_policy.predict(feature)
                old_model_predict = (old_policy.predict(feature) + np.inner(old_coeff, previous_prediction) * weight) / (1+weight)
                new_model_predict = (new_policy.predict(feature) + np.inner(new_coeff, previous_prediction) * weight) / (1+weight)
                #Y_predict[i] = (current_prediction + np.inner(coeff,previous_prediction)*weight)/(1+weight) # replace
                Y_predict[i] = interpolate * new_model_predict + (1-interpolate) * old_model_predict
    return Y_predict


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