Python numpy.inner() 使用实例

The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

def word_sim_test(filename, pos_vectors):
    delim = ','
    actual_sim_list, pred_sim_list = [], []
    missed = 0

    with open(filename, 'r') as pairs:
        for pair in pairs:
            w1, w2, actual_sim = pair.strip().split(delim)

            try:
                w1_vec = create_word_vector(w1, pos_vectors)
                w2_vec = create_word_vector(w2, pos_vectors)
                pred = float(np.inner(w1_vec, w2_vec))
                actual_sim_list.append(float(actual_sim))
                pred_sim_list.append(pred)

            except KeyError:
                missed += 1

    spearman, _ = st.spearmanr(actual_sim_list, pred_sim_list)
    pearson, _ = st.pearsonr(actual_sim_list, pred_sim_list)

    return spearman, pearson, missed 

Example 2

def vdot(a, b):
    """Returns the dot product of two vectors.

    The input arrays are flattened into 1-D vectors and then it performs inner
    product of these vectors.

    Args:
        a (cupy.ndarray): The first argument.
        b (cupy.ndarray): The second argument.

    Returns:
        cupy.ndarray: Zero-dimensional array of the dot product result.

    .. seealso:: :func:`numpy.vdot`

    """
    if a.size != b.size:
        raise ValueError('Axis dimension mismatch')
    if a.dtype.kind == 'c':
        a = a.conj()

    return core.tensordot_core(a, b, None, 1, 1, a.size, ()) 

Example 3

def idot(arrays):
    """
    Yields the cumulative array inner product (dot product) of arrays.

    Parameters
    ----------
    arrays : iterable
        Arrays to be reduced.
    
    Yields
    ------
    online_dot : ndarray

    See Also
    --------
    numpy.linalg.multi_dot : Compute the dot product of two or more arrays in a single function call, 
                             while automatically selecting the fastest evaluation order.
    """
    yield from _ireduce_linalg(arrays, np.dot) 

Example 4

def itensordot(arrays, axes = 2):
    """
    Yields the cumulative array inner product (dot product) of arrays.

    Parameters
    ----------
    arrays : iterable
        Arrays to be reduced.
    axes : int or (2,) array_like
        * integer_like: If an int N, sum over the last N axes of a 
          and the first N axes of b in order. The sizes of the corresponding axes must match.
        * (2,) array_like: Or, a list of axes to be summed over, first sequence applying to a, 
          second to b. Both elements array_like must be of the same length.
    
    Yields
    ------
    online_tensordot : ndarray

    See Also
    --------
    numpy.tensordot : Compute the tensordot on two tensors.
    """
    yield from _ireduce_linalg(arrays, np.tensordot, axes = axes) 

Example 5

def spherical_noise(gridData=None, order_max=8, spherical_harmonic_bases=None):
    ''' Returns order-limited random weights on a spherical surface

    Parameters
    ----------
    gridData : io.SphericalGrid
       SphericalGrid containing azimuth and colatitude
    order_max : int, optional
        Spherical order limit [Default: 8]

    Returns
    -------
    noisy_weights : array_like, complex
       Noisy weigths
    '''

    if spherical_harmonic_bases is None:
        if gridData is None:
            raise TypeError('Either a grid or the spherical harmonic bases have to be provided.')
        gridData = SphericalGrid(*gridData)
        spherical_harmonic_bases = sph_harm_all(order_max, gridData.azimuth, gridData.colatitude)
    else:
        order_max = _np.int(_np.sqrt(spherical_harmonic_bases.shape[1]) - 1)
    return _np.inner(spherical_harmonic_bases, _np.random.randn((order_max + 1) ** 2) + 1j * _np.random.randn((order_max + 1) ** 2)) 

Example 6

def project_verteces(self, mesh, orientation):
        """Supplement the mesh array with scalars (max and median)
        for each face projected onto the orientation vector.
        Args:
            mesh (np.array): with format face_count x 6 x 3.
            orientation (np.array): with format 3 x 3.
        Returns:
            adjusted mesh.
        """
        mesh[:, 4, 0] = np.inner(mesh[:, 1, :], orientation)
        mesh[:, 4, 1] = np.inner(mesh[:, 2, :], orientation)
        mesh[:, 4, 2] = np.inner(mesh[:, 3, :], orientation)

        mesh[:, 5, 1] = np.max(mesh[:, 4, :], axis=1)
        mesh[:, 5, 2] = np.median(mesh[:, 4, :], axis=1)
        sleep(0)  # Yield, so other threads get a bit of breathing space.
        return mesh 

Example 7

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]]]) 

Example 8

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) 

Example 9

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) 

Example 10

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 

Example 11

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) 

Example 12

def test__compute_probabilities_loosely(self):
        b = il.RoughlyOptimized(self.lists, sample_num=3)
        is_success, p, minimum = b._compute_probabilities(
            self.lists,
            self.rankings,
        )
        assert is_success
        self.assert_almost_equal(p[0], 0.0)
        self.assert_almost_equal(p[1], 0.0)
        self.assert_almost_equal(p[2], 1.0)
        self.assert_almost_equal(b._lambdas[0], 0.5 - 1.0/3)
        self.assert_almost_equal(b._lambdas[1], 0.5 - 1.0/3 + 1.0/3 - 0.0)
        self.assert_almost_equal(
            minimum,
            np.sum(b._lambdas) + np.inner(p, b._sigmas),
        )
        _, _, minimum = b._compute_probabilities_loosely(
            self.lists,
            self.rankings,
            bias_weight=10.0,
        )
        self.assert_almost_equal(
            minimum,
            10.0 * np.sum(b._lambdas) + np.inner(p, b._sigmas),
        ) 

Example 13

def test__compute_probabilities(self):
        lists = [[1, 2], [2, 3]]
        b = il.Optimized(lists, sample_num=3)
        rankings = []
        r = CreditRanking(num_rankers=len(lists), contents=[1, 2])
        r.credits = {0: {1: 1.0, 2: 0.5}, 1: {1: 1.0/3, 2: 1.0}}
        rankings.append(r)
        r = CreditRanking(num_rankers=len(lists), contents=[2, 1])
        r.credits = {0: {1: 1.0, 2: 0.5}, 1: {1: 1.0/3, 2: 1.0}}
        rankings.append(r)
        r = CreditRanking(num_rankers=len(lists), contents=[2, 3])
        r.credits = {0: {2: 0.5, 3: 1.0/3}, 1: {2: 1.0, 3: 0.5}}
        rankings.append(r)
        is_success, p, minimum = b._compute_probabilities(lists, rankings)
        assert is_success
        assert (p >= 0).all()
        assert (p <= 1).all()
        assert minimum >= 0
        self.assert_almost_equal(np.sum(p), 1)
        self.assert_almost_equal(np.inner([1-1.0/3, -0.5, -0.5], p), 0)
        self.assert_almost_equal(np.inner([0.5-1.0/3, 0.5-1.0/3, -1+1.0/3], p), 0)
        self.assert_almost_equal(p[0], 0.4285714273469387)
        self.assert_almost_equal(p[1], 0.37142857025306114)
        self.assert_almost_equal(p[2], 0.20000000240000002) 

Example 14

def find_nearest_instance_thread(test_instance_start_index, test_instance_end_index):
    
    print test_instance_start_index, test_instance_end_index
    
    for test_instance_index in range(test_instance_start_index, test_instance_end_index):
        
        # find the nearest training instance with cosine similarity
        maximal_cosine_similarity = -1
        maximal_cosine_similarity_index = 0
        for training_instance, training_instance_index in zip(training_data, range(len(training_data))):
            # compute the cosine similarity
            # first, compute the inner product
            inner_product = np.inner(test_data[test_instance_index][0].reshape(-1), training_instance[0].reshape(-1))
            normalized_inner_product = inner_product / test_data_lengths[test_instance_index] / training_data_lengths[training_instance_index]
            
            if normalized_inner_product > maximal_cosine_similarity:
                maximal_cosine_similarity = normalized_inner_product
                maximal_cosine_similarity_index = training_instance_index
        
        classified_results[test_instance_index] = maximal_cosine_similarity_index 

Example 15

def find_nearest_instance_subprocess(test_instance_start_index, test_instance_end_index,\
                                      classified_results):
    # print test_instance_start_index, test_instance_end_index
    for test_instance_index in range(test_instance_start_index, test_instance_end_index):
        # find the nearest training instance with cosine similarity
        maximal_cosine_similarity = -1.0
        maximal_cosine_similarity_index = 0
        for training_instance, training_instance_index in\
         zip(training_data_instances, range(len(training_data_instances))):
            # compute the cosine similarity
            # first, compute the inner product
            inner_product = np.inner(test_data_instances[test_instance_index], training_instance)
            # second, normalize the inner product
            normalized_inner_product = inner_product / test_data_lengths[test_instance_index]\
             / training_data_lengths[training_instance_index]
            if normalized_inner_product > maximal_cosine_similarity:
                maximal_cosine_similarity = normalized_inner_product
                maximal_cosine_similarity_index = training_instance_index
        classified_results[test_instance_index] =\
         training_data_labels[int(maximal_cosine_similarity_index)] 

Example 16

def calc_partial_factor_scores(Xscaled, Q, col_indices):
    """
    Projects individual scores onto the group-level component.
    """

    print("Calculating factor scores for datasets... ", end='')

    pfs = []

    for i, val in enumerate(col_indices):
        pfs.append(np.inner(Xscaled[:, val], Q[val, :].T))

    pfs = np.array(pfs)

    print("Done!")

    return pfs 

Example 17

def get_similar_vector(self, match_vector, match_type, num_similar,
                           oversample, normalize):
        """Get similar items from an input vector."""
        if not match_vector:
            return []

        # search_k defaults to n * n_trees in Annoy - multiply by oversample
        # don't allow oversample to go below 1, this causes errors in Annoy
        if oversample < 1:
            oversample = 1
        search_k = int(num_similar * self._annoy_objects[match_type]._ntrees *
                       oversample)

        similar_items = self._annoy_objects[match_type].get_nns_by_vector(
            match_vector, num_similar, search_k)
        # compute inner products, and sort
        scores = self.get_scores_vector(
            match_vector, match_type, similar_items, normalize)
        scores = sorted(scores, key=lambda k: k['score'], reverse=True)
        return scores[:num_similar] 

Example 18

def _get_Smatrices(self, X, y):

        Sb = np.zeros((X.shape[1], X.shape[1]))

        S = np.inner(X.T, X.T)
        N = len(X)
        mu = np.mean(X, axis=0)
        classLabels = np.unique(y)
        for label in classLabels:
            classIdx = np.argwhere(y == label).T[0]
            Nl = len(classIdx)
            xL = X[classIdx]
            muL = np.mean(xL, axis=0)
            muLbar = muL - mu
            Sb = Sb + Nl * np.outer(muLbar, muLbar)

        Sbar = S - N * np.outer(mu, mu)
        Sw = Sbar - Sb
        self.mean_ = mu

        return (Sw, Sb) 

Example 19

def convex_hull(points, vind, nind, tind, obj):
	"super ineffective"
	cnt = len(points)
	for a in range(cnt):
		for b in range(a+1,cnt):
			for c in range(b+1,cnt):
				vec1 = points[a] - points[b]
				vec2 = points[a] - points[c]
				n  = np.cross(vec1, vec2)
				n /= np.linalg.norm(n)
				C = np.dot(n, points[a])
				inner = np.inner(n, points)
				pos = (inner <= C+0.0001).all()
				neg = (inner >= C-0.0001).all()
				if not pos and not neg: continue
				obj.out.write("f %i//%i %i//%i %i//%i\n" % ( 
					(vind[a], nind[a], vind[b], nind[b], vind[c], nind[c])
					if (inner - C).sum() < 0 else
					(vind[a], nind[a], vind[c], nind[c], vind[b], nind[b]) ) )
				#obj.out.write("f %i/%i/%i %i/%i/%i %i/%i/%i\n" % ( 
				#	(vind[a], tind[a], nind[a], vind[b], tind[b], nind[b], vind[c], tind[c], nind[c])
				#	if (inner - C).sum() < 0 else
				#	(vind[a], tind[a], nind[a], vind[c], tind[c], nind[c], vind[b], tind[b], nind[b]) ) ) 

Example 20

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]]]) 

Example 21

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) 

Example 22

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) 

Example 23

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 

Example 24

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) 

Example 25

def vdot(a, b):
    """Returns the dot product of two vectors.

    The input arrays are flattened into 1-D vectors and then it performs inner
    product of these vectors.

    Args:
        a (cupy.ndarray): The first argument.
        b (cupy.ndarray): The second argument.

    Returns:
        cupy.ndarray: Zero-dimensional array of the dot product result.

    .. seealso:: :func:`numpy.vdot`

    """
    if a.size != b.size:
        raise ValueError('Axis dimension mismatch')

    return core.tensordot_core(a, b, None, 1, 1, a.size, ()) 

Example 26

def transform(self, X):
        """
        Project the data so as to maximize class separation (large separation
        between projected class means and small variance within each class).

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]

        Returns
        -------
        X_new : array, shape = [n_samples, n_components_found_]
        """

        #X = np.asarray(X)
        #ts = time.time()
        k = self._get_kernel(X, self.X_fit_)
        #if self.print_timing: print 'KernelFisher.transform: k took', time.time() - ts

        #ts = time.time()
        z = np.inner(self.Z, (k-self.K_mean) ).T
        #if self.print_timing: print 'KernelFisher.transform: z took', time.time() - ts

        return z 

Example 27

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]]]) 

Example 28

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) 

Example 29

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) 

Example 30

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 

Example 31

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) 

Example 32

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 

Example 33

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 

Example 34

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 

Example 35

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]]]) 

Example 36

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) 

Example 37

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) 

Example 38

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 

Example 39

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) 

Example 40

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. 

Example 41

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]]]) 

Example 42

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) 

Example 43

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) 

Example 44

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 

Example 45

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) 

Example 46

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.) 

Example 47

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) 

Example 48

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) 

Example 49

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 

Example 50

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 
点赞

发表评论

电子邮件地址不会被公开。 必填项已用*标注