python类equal()的实例源码

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()

def fail_if_equal(actual, desired, err_msg='',):
    """
    Raises an assertion error if two items are equal.

    """
    if isinstance(desired, dict):
        if not isinstance(actual, dict):
            raise AssertionError(repr(type(actual)))
        fail_if_equal(len(actual), len(desired), err_msg)
        for k, i in desired.items():
            if k not in actual:
                raise AssertionError(repr(k))
            fail_if_equal(actual[k], desired[k], 'key=%r\n%s' % (k, err_msg))
        return
    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
        fail_if_equal(len(actual), len(desired), err_msg)
        for k in range(len(desired)):
            fail_if_equal(actual[k], desired[k], 'item=%r\n%s' % (k, err_msg))
        return
    if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
        return fail_if_array_equal(actual, desired, err_msg)
    msg = build_err_msg([actual, desired], err_msg)
    if not desired != actual:
        raise AssertionError(msg)

def grayscaleimage(self, value):
        try:
            if value.ndim == 2:
                self._grayscaleimage = value
                if (_np.equal(self._x,None).any() or
                    _np.equal(self._y,None).any() or
                    self._x.size != value.shape[1] or
                    self._y.size != value.shape[0]):

                    self._x = _np.linspace(1, value.shape[1], value.shape[1])
                    self._y = _np.linspace(1, value.shape[0], value.shape[0])
                    self.xunits = self.XUNITS
                    self.yunits = self.YUNITS

                else:
                    pass
        except:
            pass

def paren_data(T, n_data):
    MAX_COUNT = 10
    n_paren = 10
    n_noise = 10

    inputs = (np.random.rand(T, n_data)* (n_paren * 2 + n_noise)).astype(np.int32)
    counts = np.zeros((n_data, n_paren), dtype=np.int32)
    targets = np.zeros((T, n_data, n_paren), dtype = np.int32)
    opening_parens = (np.arange(0, n_paren)*2)[None, :]
    closing_parens = opening_parens + 1
    for i in range(T):
        opened = np.equal(inputs[i, :, None], opening_parens)
        counts = np.minimum(MAX_COUNT, counts + opened)
        closed = np.equal(inputs[i, :, None], closing_parens)
        counts = np.maximum(0, counts - closed)
        targets[i, :, :] = counts


    x = np.transpose(inputs, [1,0])
    y = np.transpose(targets, [1,0,2])

    return x, y

def is_connect_exist_nn(node_in, node_out, nn):
        """
        check if the connection between node_in and node_out exists

        :param node_in:
        :param node_out:
        :param nn: Neural network instance
        :return: True if exists, False if DNE
        """

        assert type(nn) == NeuralNetwork, "nn must be an instance of Neural Network"

        if nn.connect_genes is None:
            return False

        connect = [node_in, node_out]
        history = nn.connect_genes[:, :2]

        return any(np.equal(connect, history).all(1))

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)

def not_equal(x1, x2):
    """
    Return (x1 != x2) element-wise.

    Unlike `numpy.not_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, greater_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '!=', True)

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)

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)

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)

def test_scalar_none_comparison(self):
        # Scalars should still just return false and not give a warnings.
        # The comparisons are flagged by pep8, ignore that.
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', FutureWarning)
            assert_(not np.float32(1) == None)
            assert_(not np.str_('test') == None)
            # This is dubious (see below):
            assert_(not np.datetime64('NaT') == None)

            assert_(np.float32(1) != None)
            assert_(np.str_('test') != None)
            # This is dubious (see below):
            assert_(np.datetime64('NaT') != None)
        assert_(len(w) == 0)

        # For documentaiton purpose, this is why the datetime is dubious.
        # At the time of deprecation this was no behaviour change, but
        # it has to be considered when the deprecations is done.
        assert_(np.equal(np.datetime64('NaT'), None))

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()

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()

def fail_if_equal(actual, desired, err_msg='',):
    """
    Raises an assertion error if two items are equal.

    """
    if isinstance(desired, dict):
        if not isinstance(actual, dict):
            raise AssertionError(repr(type(actual)))
        fail_if_equal(len(actual), len(desired), err_msg)
        for k, i in desired.items():
            if k not in actual:
                raise AssertionError(repr(k))
            fail_if_equal(actual[k], desired[k], 'key=%r\n%s' % (k, err_msg))
        return
    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
        fail_if_equal(len(actual), len(desired), err_msg)
        for k in range(len(desired)):
            fail_if_equal(actual[k], desired[k], 'item=%r\n%s' % (k, err_msg))
        return
    if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
        return fail_if_array_equal(actual, desired, err_msg)
    msg = build_err_msg([actual, desired], err_msg)
    if not desired != actual:
        raise AssertionError(msg)

def get_same_status(pairs, items, target):
    text_compare = pairs
    item1 = items[['itemID', target]]
    item1 = item1.rename(
        columns={
            'itemID': 'itemID_1',
            target: target + '_1',
        }
    )
    text_compare = pd.merge(text_compare, item1, how='left', on='itemID_1', left_index=True)
    item2 = items[['itemID', target]]
    item2 = item2.rename(
        columns={
            'itemID': 'itemID_2',
            target: target + '_2',
        }
    )
    text_compare = pd.merge(text_compare, item2, how='left', on='itemID_2', left_index=True)
    text_compare[target + '_same'] = np.equal(text_compare[target + '_1'], text_compare[target + '_2']).astype(np.int32)
    # print(text_compare[target + '_same'].describe())
    return text_compare[['id', target + '_same']]

def gen_hull(p, p_mask, f_encode, f_probi, options):
    # p: n_sizes * n_samples * data_dim
    n_sizes = p.shape[0]
    n_samples = p.shape[1] if p.ndim == 3 else 1
    hprev = f_encode(p_mask, p)  # n_sizes * n_samples * data_dim
    points = numpy.zeros((n_samples, n_sizes), dtype='int64')
    h = hprev[-1]
    c = numpy.zeros((n_samples, options['dim_proj']), dtype=config.floatX)
    xi = numpy.zeros((n_samples,), dtype='int64')
    xi_mask = numpy.ones((n_samples,), dtype=config.floatX)
    for i in range(n_sizes):
        h, c, probi = f_probi(p_mask[i], xi, h, c, hprev, p_mask, p)
        xi = probi.argmax(axis=0)
        xi *= xi_mask.astype(numpy.int64)  # Avoid compatibility problem in numpy 1.10
        xi_mask = (numpy.not_equal(xi, 0)).astype(config.floatX)
        if numpy.equal(xi_mask, 0).all():
            break
        points[:, i] = xi
    return points

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

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)

def not_equal(x1, x2):
    """
    Return (x1 != x2) element-wise.

    Unlike `numpy.not_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, greater_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '!=', True)

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)