Python numpy.equal方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def test_equal():
    """Test for logical greater in onnx operators."""
    input1 = np.random.rand(1, 3, 4, 5).astype("float32")
    input2 = np.random.rand(1, 5).astype("float32")
    inputs = [helper.make_tensor_value_info("input1", TensorProto.FLOAT, shape=(1, 3, 4, 5)),
              helper.make_tensor_value_info("input2", TensorProto.FLOAT, shape=(1, 5))]

    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=(1, 3, 4, 5))]

    nodes = [helper.make_node("Equal", ["input1", "input2"], ["output"])]

    graph = helper.make_graph(nodes,
                              "equal_test",
                              inputs,
                              outputs)

    greater_model = helper.make_model(graph)
    
    bkd_rep = mxnet_backend.prepare(greater_model)
    numpy_op = np.equal(input1, input2).astype(np.float32)
    output = bkd_rep.run([input1, input2])
    npt.assert_almost_equal(output[0], numpy_op) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def zdivide(a, b, null=0):
    '''
    zdivide(a, b) returns the quotient a / b as a numpy array object. Unlike numpy's divide function
      or a/b syntax, zdivide will thread over the earliest dimension possible; thus if a.shape is
      (4,2) and b.shape is 4, zdivide(a,b) is a equivalent to [ai*zinv(bi) for (ai,bi) in zip(a,b)].

    The optional argument null (default: 0) may be given to specify that zeros in the arary b should
    instead be replaced with the given value in the result. Note that if this value is not equal to
    0, then any sparse array passed as argument b must be reified.

    The zdivide function never raises an error due to divide-by-zero; if you desire this behavior,
    use the divide function instead.

    Note that zdivide(a,b, null=z) is not quite equivalent to a*zinv(b, null=z) unless z is 0; if z
    is not zero, then the same elements that are zet to z in zinv(b, null=z) are set to z in the
    result of zdivide(a,b, null=z) rather than the equivalent element of a times z.
    '''
    (a,b) = unbroadcast(a,b)
    return czdivide(a,b, null=null) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def feedforward(self, X):
        output = super().feedforward(X)

        for t in range(1, self.time + 1):
            time_gate = np.equal(t % self.tick_array, 0.)
            Z = np.concatenate((self.inputs[t - 1], output), axis=-1)
            gated_W = self.weights * time_gate[None, :]
            gated_b = self.biases * time_gate
            output = self.activation.forward(Z.dot(gated_W) + gated_b)

            self.Zs.append(Z)
            self.gates.append([time_gate, gated_W])
            self.cache.append(output)

        if self.return_seq:
            self.output = np.stack(self.cache, axis=1)
        else:
            self.output = self.cache[-1]

        return self.output 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def VOCap(rec,prec):

    mpre = np.zeros([1,2+len(prec)])
    mpre[0,1:len(prec)+1] = prec
    mrec = np.zeros([1,2+len(rec)])
    mrec[0,1:len(rec)+1] = rec
    mrec[0,len(rec)+1] = 1.0

    for i in range(mpre.size-2,-1,-1):
        mpre[0,i] = max(mpre[0,i],mpre[0,i+1])

    i = np.argwhere( ~np.equal( mrec[0,1:], mrec[0,:mrec.shape[1]-1]) )+1
    i = i.flatten()

    # compute area under the curve
    ap = np.sum( np.multiply( np.subtract( mrec[0,i], mrec[0,i-1]), mpre[0,i] ) )

    return ap 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8):
    """
    Returns true if all components of a and b are equal to given tolerances.

    If fill_value is True, masked values considered equal. Otherwise,
    masked values are considered unequal.  The relative error rtol should
    be positive and << 1.0 The absolute error atol comes into play for
    those elements of b that are very small or zero; it says how small a
    must be also.

    """
    m = mask_or(getmask(a), getmask(b))
    d1 = filled(a)
    d2 = filled(b)
    if d1.dtype.char == "O" or d2.dtype.char == "O":
        return np.equal(d1, d2).ravel()
    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
    d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y))
    return d.ravel() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def almost(a, b, decimal=6, fill_value=True):
    """
    Returns True if a and b are equal up to decimal places.

    If fill_value is True, masked values considered equal. Otherwise,
    masked values are considered unequal.

    """
    m = mask_or(getmask(a), getmask(b))
    d1 = filled(a)
    d2 = filled(b)
    if d1.dtype.char == "O" or d2.dtype.char == "O":
        return np.equal(d1, d2).ravel()
    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
    d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
    return d.ravel() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def test_subclass_that_overrides_eq(self):
        # While we cannot guarantee testing functions will always work for
        # subclasses, the tests should ideally rely only on subclasses having
        # comparison operators, not on them being able to store booleans
        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
        class MyArray(np.ndarray):
            def __eq__(self, other):
                return bool(np.equal(self, other).all())

            def __ne__(self, other):
                return not self == other

        a = np.array([1., 2.]).view(MyArray)
        b = np.array([2., 3.]).view(MyArray)
        assert_(type(a == a), bool)
        assert_(a == a)
        assert_(a != b)
        self._test_equal(a, a)
        self._test_not_equal(a, b)
        self._test_not_equal(b, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def test_error_message(self):
        with pytest.raises(AssertionError) as exc_info:
            self._assert_func(np.array([1, 2]), np.array([[1, 2]]))
        msg = str(exc_info.value)
        msg2 = msg.replace("shapes (2L,), (1L, 2L)", "shapes (2,), (1, 2)")
        msg_reference = textwrap.dedent("""\

        Arrays are not equal

        (shapes (2,), (1, 2) mismatch)
         x: array([1, 2])
         y: array([[1, 2]])""")

        try:
            assert_equal(msg, msg_reference)
        except AssertionError:
            assert_equal(msg2, msg_reference) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def test_ignore_object_identity_in_equal(self):
        # Check error raised when comparing identical objects whose comparison
        # is not a simple boolean, e.g., arrays that are compared elementwise.
        a = np.array([np.array([1, 2, 3]), None], dtype=object)
        assert_raises(ValueError, np.equal, a, a)

        # Check error raised when comparing identical non-comparable objects.
        class FunkyType(object):
            def __eq__(self, other):
                raise TypeError("I won't compare")

        a = np.array([FunkyType()])
        assert_raises(TypeError, np.equal, a, a)

        # Check identity doesn't override comparison mismatch.
        a = np.array([np.nan], dtype=object)
        assert_equal(np.equal(a, a), [False]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def equal(x1, x2):
    """
    Return (x1 == x2) element-wise.

    Unlike `numpy.equal`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    not_equal, greater_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '==', True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def greater_equal(x1, x2):
    """
    Return (x1 >= x2) element-wise.

    Unlike `numpy.greater_equal`, this comparison is performed by
    first stripping whitespace characters from the end of the string.
    This behavior is provided for backward-compatibility with
    numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '>=', True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def less_equal(x1, x2):
    """
    Return (x1 <= x2) element-wise.

    Unlike `numpy.less_equal`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, greater, less
    """
    return compare_chararrays(x1, x2, '<=', True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def greater(x1, x2):
    """
    Return (x1 > x2) element-wise.

    Unlike `numpy.greater`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, less_equal, less
    """
    return compare_chararrays(x1, x2, '>', True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import equal [as 別名]
def less(x1, x2):
    """
    Return (x1 < x2) element-wise.

    Unlike `numpy.greater`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, less_equal, greater
    """
    return compare_chararrays(x1, x2, '<', True)