Python numpy.array_equiv函数代码示例

 def test_copy(self):
     g = self.Grid
     h = g.copy()
     npt.assert_(np.array_equiv(g.domain[-1] - h.domain[-1], 0))
     g.domain[-1] += 1
     npt.assert_(not np.array_equiv(g.domain[-1] - h.domain[-1], 0))
     return 0

def test_RGB():
    """
    Test multi channel light class
    """
    position = 10, 10
    size = 50, 50
    channels = 3
    light = pylights.MultiLight('single_test', position, size, channels=channels)
    old_value = np.array([0.8, 0.3, 0.6])
    new_value = np.array([0.1, 0.1, 0.1])
    light.set(old_value, False)
    assert np.array_equiv(light.value_current, old_value)
    assert np.array_equiv(light.value_target, old_value)
    assert np.array_equiv(light.value_output, np.array([0, 0, 0]))
    light.set(new_value)
    assert np.array_equiv(light.value_current, old_value)
    assert np.array_equiv(light.value_target, new_value)
    assert np.array_equiv(light.value_output, np.array([0, 0, 0]))
    light.update()
    temp = (old_value + light.damping * (new_value - old_value))
    assert np.array_equiv(light.value_current, temp)
    assert np.array_equiv(light.value_target, new_value)
    assert np.array_equiv(light.value_output, (old_value * 255.0).astype(int))
    light.update()
    assert np.array_equiv(light.value_output, (temp * 255.0).astype(int))

  def __testSequences(self, device, module):
    module = str(module)
    # Basic Interface: Currently supports read of all sequences only
    #device.write("", "WORD_ADC_ENA", 1)
    # Arrange the data on the card:
    predefinedSequence = numpy.array([0x00010000,
                                      0x00030002,
                                      0x00050004,
                                      0x00070006,
                                      0x00090008,
                                      0x000b000a,
                                      0x000d000c,
                                      0x000f000e,
                                      0x00110010,
                                      0x00130012,
                                      0x00150014,
                                      0x00170016,
                                      0x00ff0018], dtype=numpy.int32)
    device.write_raw(module, 'AREA_DMAABLE', predefinedSequence)

    expectedMatrix = numpy.array([[0,  1,  2,  3],
                                  [4,  5,  6,  7],
                                  [8, 9, 10, 11],
                                  [12, 13, 14, 15],
                                  [16, 17, 18, 19],
                                  [20, 21, 22, 23]], dtype=numpy.float32)
    readInMatrix = device.read_sequences(module, 'DMA')
    self.assertTrue(numpy.array_equiv(readInMatrix, expectedMatrix))
    self.assertTrue(readInMatrix.dtype == numpy.float32)
    readInMatrix = device.read_sequences(registerPath = '/' + str(module)+ '/DMA')
    self.assertTrue(numpy.array_equiv(readInMatrix, expectedMatrix))
    self.assertTrue(readInMatrix.dtype == numpy.float32)

    def bin2d_cvt_equal_mass(self, wvt=None, verbose=1) :
        """
        Produce a CV Tesselation

        wvt: default is None (will use preset value, see self.wvt)
        """

        ## Reset the status and statusnode for all nodes
        self.status = np.zeros(self.npix, dtype=Nint)
        self.statusnode = np.arange(self.xnode.size) + 1

        if wvt is not None : self.wvt = wvt
        if self.wvt : self.weight = np.ones_like(self.data)
        else : self.weight = self.data**4

        self.scale = 1.0

        self.niter = 0
        ## WHILE LOOP: stop when the nodes do not move anymore ============
        Oldxnode, Oldynode = copy.copy(self.xnode[-1]), copy.copy(self.ynode[-1])
        while (not np.array_equiv(self.xnode, Oldxnode)) | (not np.array_equiv(self.ynode, Oldynode)):
            Oldxnode, Oldynode = copy.copy(self.xnode), copy.copy(self.ynode)
            ## Assign the closest centroid to each bin
            self.bin2d_assign_bins()

            ## New nodes weighted centroids
            self.bin2d_weighted_centroid()

            ## Eq. (4) of Diehl & Statler (2006)
            if self.wvt : self.scale = sqrt(self.Areanode/self.flux_node)
            self.niter += 1

    def equiv(self, other):
        """Test if other is an equivalent weighting.

        Returns
        -------
        equivalent : `bool`
            `True` if other is a `WeightingBase` instance with the same
            `WeightingBase.impl`, which yields the same result as this
            weighting for any input, `False` otherwise. This is checked
            by entry-wise comparison of matrices/vectors/constants.
        """
        # Optimization for equality
        if self == other:
            return True

        elif self.exponent != getattr(other, 'exponent', -1):
            return False

        elif isinstance(other, MatrixWeightingBase):
            if self.matrix.shape != other.matrix.shape:
                return False

            if self.matrix_issparse:
                if other.matrix_issparse:
                    # Optimization for different number of nonzero elements
                    if self.matrix.nnz != other.matrix.nnz:
                        return False
                    else:
                        # Most efficient out-of-the-box comparison
                        return (self.matrix != other.matrix).nnz == 0
                else:  # Worst case: compare against dense matrix
                    return np.array_equal(self.matrix.todense(), other.matrix)

            else:  # matrix of `self` is dense
                if other.matrix_issparse:
                    return np.array_equal(self.matrix, other.matrix.todense())
                else:
                    return np.array_equal(self.matrix, other.matrix)

        elif isinstance(other, VectorWeightingBase):
            if self.matrix_issparse:
                return (np.array_equiv(self.matrix.diagonal(),
                                       other.vector) and
                        np.array_equal(self.matrix.asformat('dia').offsets,
                                       np.array([0])))
            else:
                return np.array_equal(
                    self.matrix, other.vector * np.eye(self.matrix.shape[0]))

        elif isinstance(other, ConstWeightingBase):
            if self.matrix_issparse:
                return (np.array_equiv(self.matrix.diagonal(), other.const) and
                        np.array_equal(self.matrix.asformat('dia').offsets,
                                       np.array([0])))
            else:
                return np.array_equal(
                    self.matrix, other.const * np.eye(self.matrix.shape[0]))
        else:
            return False

def test_xls_contents():
    sample = Sample.from_file(from_current_dir('sample2.xlsx'))
    expected_attributes = array([[1], [2], [3], [4], [5]])
    expected_categories = array([1, 2, 3, 4, 5])
    expected_columns = ["Age"]
    assert array_equiv(sample.attributes, expected_attributes)
    assert array_equiv(sample.categories, expected_categories)
    assert sample.columns == expected_columns

def test_from_file_with_indices():
    sample = Sample.from_file(from_current_dir('sample2.xlsx'), [0, 4])
    expected_attributes = array([[1], [5]])
    expected_categories = array([1, 5])
    expected_columns = ["Age"]
    assert array_equiv(sample.attributes, expected_attributes)
    assert array_equiv(sample.categories, expected_categories)
    assert sample.columns == expected_columns

 def test_equivalent(self):
     """
     Returns True if input arrays are shape consistent and all elements equal.
     Shape consistent means they are either the same shape, 
     or one input array can be broadcasted to create the same shape as the other one.
     """
     a = [1,2]
     b = np.asarray(a)
     self.assertTrue(np.array_equiv(a, b))
     self.assertTrue(np.array_equiv(a,[a,a]))

def test_transform():
    sample = Sample.from_file(from_current_dir('sample3.xlsx'))
    sample.merge_columns(['One', 'Three'], TestClusterer(), 'New')

    expected_attributes = array([[2, 0], [2, 0]])
    expected_categories = array([0, 0])
    expected_columns = ['Two', 'New']
    assert array_equiv(sample.attributes, expected_attributes)
    assert array_equiv(sample.categories, expected_categories)
    assert sample.columns == expected_columns

def test_remove_existing_column():
    sample = Sample.from_file(from_current_dir('sample3.xlsx'))
    sample.remove_column('Two')

    expected_attributes = array([[1, 3], [1, 3]])
    expected_categories = array([0, 0])
    expected_columns = ['One', 'Three']
    assert array_equiv(sample.attributes, expected_attributes)
    assert array_equiv(sample.categories, expected_categories)
    assert sample.columns == expected_columns

def test_normalize_existing_column():
    sample = Sample.from_file(from_current_dir('sample2.xlsx'))
    normalizer = sample.get_normalizer((0.0, 1.0))
    sample.normalize(normalizer, ["Age"])

    expected_attributes = array([[0.0], [0.25], [0.5], [0.75], [1.0]])
    expected_categories = array([1, 2, 3, 4, 5])
    expected_columns = ["Age"]
    assert array_equiv(sample.attributes, expected_attributes)
    assert array_equiv(sample.categories, expected_categories)
    assert sample.columns == expected_columns

def _checkSameExperimentResults(exp1, exp2):
    """ Returns False if experiments gave same results, true if they match. """
    if not np.array_equiv(exp1.result["learning_steps"], exp2.result["learning_steps"]):
        # Same number of steps before failure (where applicable)
        return False
    if not np.array_equiv(exp1.result["return"], exp2.result["return"]):
        # Same return on each test episode
        return False
    if not np.array_equiv(exp1.result["steps"], exp2.result["steps"]):
        # Same number of steps taken on each training episode
        return False
    return True

def _equal(a, b):
    #recursion on subclasses of types: tuple, list, dict
    #specifically checks             : float, ndarray
    if type(a) is float and type(b) is float:#float
        return(numpy.allclose(a, b))
    elif type(a) is numpy.ndarray and type(b) is numpy.ndarray:#ndarray
        return(numpy.array_equiv(a, b))#alternative for float-arrays: numpy.allclose(a, b[, rtol, atol])
    elif isinstance(a, dict) and isinstance(b, dict):#dict
        if len(a) != len(b):
            return(False)
        t = True
        for key, val in a.items():
            if key not in b:
                return(False)
            t = _equal(val, b[key])
            if not t:
                return(False)
        return(t)
    elif (isinstance(a, list) and isinstance(b, list)) or (isinstance(a, tuple) and isinstance(b, tuple)):#list, tuples
        if len(a) != len(b):
            return(False)
        t = True
        for vala, valb in zip(a, b):
            t = _equal(vala, valb)
            if not t:
                return(False)
        return(t)
    else:#fallback
        return(a == b)

def test_agglomerative_clustering_with_distance_threshold(linkage):
    # Check that we obtain the correct number of clusters with
    # agglomerative clustering with distance_threshold.
    rng = np.random.RandomState(0)
    mask = np.ones([10, 10], dtype=np.bool)
    n_samples = 100
    X = rng.randn(n_samples, 50)
    connectivity = grid_to_graph(*mask.shape)
    # test when distance threshold is set to 10
    distance_threshold = 10
    for conn in [None, connectivity]:
        clustering = AgglomerativeClustering(
            n_clusters=None,
            distance_threshold=distance_threshold,
            connectivity=conn, linkage=linkage)
        clustering.fit(X)
        clusters_produced = clustering.labels_
        num_clusters_produced = len(np.unique(clustering.labels_))
        # test if the clusters produced match the point in the linkage tree
        # where the distance exceeds the threshold
        tree_builder = _TREE_BUILDERS[linkage]
        children, n_components, n_leaves, parent, distances = \
            tree_builder(X, connectivity=conn, n_clusters=None,
                         return_distance=True)
        num_clusters_at_threshold = np.count_nonzero(
            distances >= distance_threshold) + 1
        # test number of clusters produced
        assert num_clusters_at_threshold == num_clusters_produced
        # test clusters produced
        clusters_at_threshold = _hc_cut(n_clusters=num_clusters_produced,
                                        children=children,
                                        n_leaves=n_leaves)
        assert np.array_equiv(clusters_produced,
                              clusters_at_threshold)

 def exp(xi,tau=None):
     #given a 6x1 vector returns a SE3 object
     #may output garbage if matrix not invertible
     c=np.zeros((3,1))
     xiHat=SE3.hat(xi)
     v=np.array([[xiHat[0,3]],
                 [xiHat[1,3]],
                 [xiHat[2,3]]])
     w=np.array([[xiHat[2,1]],
                 [xiHat[0,2]],
                 [xiHat[1,0]]])
     wtrans=w.T
     what=xiHat[0:3,0:3]
     normw=np.linalg.norm(w)
     w2=w.dot(wtrans)-math.pow(normw,2)*np.eye(3)
     if tau==None:
         tau=1
         print tau
     if np.array_equiv(w,c)==False:
         ewt=np.eye(3)+(what/normw)*math.sin(tau*normw)+w2*(1-math.cos(normw*tau))/math.pow(normw,2)
         d1=np.eye(3)-ewt
         d2=d1.dot(what)/math.pow(normw,2)
         d3=d2.dot(v)
         d32=(w.dot(wtrans).dot(v)*tau)/math.pow(normw,2)
         d=d3+d32
     else:
         ewt=np.eye(3)
         d=v*tau
     expXi=np.concatenate((np.concatenate((ewt,d),axis=1),np.array([[0,0,0,1]])),axis=0)
     omegatau=SE3()
     omegatau.__M=expXi
     return omegatau