Python numpy.equal() 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 go_nn_kdtree(eps=0, parallel=True):
    """
    Using a specialized data structure, the KDTree

    This is not as performant because we're in a high dimensional space

    0.777 accuracy? Should be 0.794
    """
    n_jobs = 1
    if parallel:
        n_jobs = -1

    neighbors = tree.query(Xtest, eps=eps, n_jobs=n_jobs)
    predictions = ytrain[neighbors[1]]

    acc = np.equal(predictions, ytest).mean()
    return acc 

Example 2

def updateSpots(self, dataSet=None):
        if dataSet is None:
            dataSet = self.data

        invalidate = False
        if self.opts['pxMode']:
            mask = np.equal(dataSet['sourceRect'], None)
            if np.any(mask):
                invalidate = True
                opts = self.getSpotOpts(dataSet[mask])
                sourceRect = self.fragmentAtlas.getSymbolCoords(opts)
                dataSet['sourceRect'][mask] = sourceRect

            self.fragmentAtlas.getAtlas() # generate atlas so source widths are available.

            dataSet['width'] = np.array(list(imap(QtCore.QRectF.width, dataSet['sourceRect'])))/2
            dataSet['targetRect'] = None
            self._maxSpotPxWidth = self.fragmentAtlas.max_width
        else:
            self._maxSpotWidth = 0
            self._maxSpotPxWidth = 0
            self.measureSpotSizes(dataSet)

        if invalidate:
            self.invalidate() 

Example 3

def getSpotOpts(self, recs, scale=1.0):
        if recs.ndim == 0:
            rec = recs
            symbol = rec['symbol']
            if symbol is None:
                symbol = self.opts['symbol']
            size = rec['size']
            if size < 0:
                size = self.opts['size']
            pen = rec['pen']
            if pen is None:
                pen = self.opts['pen']
            brush = rec['brush']
            if brush is None:
                brush = self.opts['brush']
            return (symbol, size*scale, fn.mkPen(pen), fn.mkBrush(brush))
        else:
            recs = recs.copy()
            recs['symbol'][np.equal(recs['symbol'], None)] = self.opts['symbol']
            recs['size'][np.equal(recs['size'], -1)] = self.opts['size']
            recs['size'] *= scale
            recs['pen'][np.equal(recs['pen'], None)] = fn.mkPen(self.opts['pen'])
            recs['brush'][np.equal(recs['brush'], None)] = fn.mkBrush(self.opts['brush'])
            return recs 

Example 4

def updateSpots(self, dataSet=None):
        if dataSet is None:
            dataSet = self.data

        invalidate = False
        if self.opts['pxMode']:
            mask = np.equal(dataSet['sourceRect'], None)
            if np.any(mask):
                invalidate = True
                opts = self.getSpotOpts(dataSet[mask])
                sourceRect = self.fragmentAtlas.getSymbolCoords(opts)
                dataSet['sourceRect'][mask] = sourceRect

            self.fragmentAtlas.getAtlas() # generate atlas so source widths are available.

            dataSet['width'] = np.array(list(imap(QtCore.QRectF.width, dataSet['sourceRect'])))/2
            dataSet['targetRect'] = None
            self._maxSpotPxWidth = self.fragmentAtlas.max_width
        else:
            self._maxSpotWidth = 0
            self._maxSpotPxWidth = 0
            self.measureSpotSizes(dataSet)

        if invalidate:
            self.invalidate() 

Example 5

def getSpotOpts(self, recs, scale=1.0):
        if recs.ndim == 0:
            rec = recs
            symbol = rec['symbol']
            if symbol is None:
                symbol = self.opts['symbol']
            size = rec['size']
            if size < 0:
                size = self.opts['size']
            pen = rec['pen']
            if pen is None:
                pen = self.opts['pen']
            brush = rec['brush']
            if brush is None:
                brush = self.opts['brush']
            return (symbol, size*scale, fn.mkPen(pen), fn.mkBrush(brush))
        else:
            recs = recs.copy()
            recs['symbol'][np.equal(recs['symbol'], None)] = self.opts['symbol']
            recs['size'][np.equal(recs['size'], -1)] = self.opts['size']
            recs['size'] *= scale
            recs['pen'][np.equal(recs['pen'], None)] = fn.mkPen(self.opts['pen'])
            recs['brush'][np.equal(recs['brush'], None)] = fn.mkBrush(self.opts['brush'])
            return recs 

Example 6

def calc_metrics(self, data_gen, history, dataset, logs):
        y_true = []
        predictions = []
        for i in range(data_gen.steps):
            if self.verbose == 1:
                print "\r\tdone {}/{}".format(i, data_gen.steps),
            (x,y) = next(data_gen)
            pred = self.model.predict(x, batch_size=self.batch_size)
            if isinstance(x, list) and len(x) == 2: # deep supervision
                for m, t, p in zip(x[1].flatten(), y.flatten(), pred.flatten()):
                    if np.equal(m, 1):
                        y_true.append(t)
                        predictions.append(p)
            else:
                y_true += list(y.flatten())
                predictions += list(pred.flatten())
        print "\n"
        predictions = np.array(predictions)
        predictions = np.stack([1-predictions, predictions], axis=1)
        ret = metrics.print_metrics_binary(y_true, predictions)
        for k, v in ret.iteritems():
            logs[dataset + '_' + k] = v
        history.append(ret) 

Example 7

def add(self, output, target):
        if torch.is_tensor(output):
            output = output.cpu().squeeze().numpy()
        if torch.is_tensor(target):
            target = target.cpu().squeeze().numpy()
        elif isinstance(target, numbers.Number):
            target = np.asarray([target])
        assert np.ndim(output) == 1, \
            'wrong output size (1D expected)'
        assert np.ndim(target) == 1, \
            'wrong target size (1D expected)'
        assert output.shape[0] == target.shape[0], \
            'number of outputs and targets does not match'
        assert np.all(np.add(np.equal(target, 1), np.equal(target, 0))), \
            'targets should be binary (0, 1)'

        self.scores = np.append(self.scores, output)
        self.targets = np.append(self.targets, target) 

Example 8

def validate_transitions_cpu_old(transitions, **kwargs):
    pre = np.array(transitions[0])
    suc = np.array(transitions[1])
    base = setting['base']
    width  = pre.shape[1] // base
    height = pre.shape[1] // base
    load(width,height)

    pre_validation = validate_states(pre, **kwargs)
    suc_validation = validate_states(suc, **kwargs)

    results = []
    for pre, suc, pre_validation, suc_validation in zip(pre, suc, pre_validation, suc_validation):
        
        if pre_validation and suc_validation:
            c = to_configs(np.array([pre, suc]), verbose=False)
            succs = successors(c[0], width, height)
            results.append(np.any(np.all(np.equal(succs, c[1]), axis=1)))
        else:
            results.append(False)
    
    return results 

Example 9

def setup():
    setting['base'] = 14

    def loader(width,height):
        from ..util.mnist import mnist
        base = setting['base']
        x_train, y_train, _, _ = mnist()
        filters = [ np.equal(i,y_train) for i in range(9) ]
        imgs    = [ x_train[f] for f in filters ]
        panels  = [ imgs[0].reshape((28,28)) for imgs in imgs ]
        panels[8] = imgs[8][3].reshape((28,28))
        panels[1] = imgs[8][3].reshape((28,28))
        panels = np.array(panels)
        stepy = panels.shape[1]//base
        stepx = panels.shape[2]//base
        # unfortunately the method below generates "bolder" fonts
        # panels = panels[:,:stepy*base,:stepx*base,]
        # panels = panels.reshape((panels.shape[0],base,stepy,base,stepx))
        # panels = panels.mean(axis=(2,4))
        # panels = panels.round()
        panels = panels[:,::stepy,::stepx][:,:base,:base].round()
        panels = preprocess(panels)
        return panels

    setting['loader'] = loader 

Example 10

def validate_transitions(transitions, check_states=True, **kwargs):
    pre = np.array(transitions[0])
    suc = np.array(transitions[1])

    if check_states:
        pre_validation = validate_states(pre, verbose=False, **kwargs)
        suc_validation = validate_states(suc, verbose=False, **kwargs)

    pre_configs = to_configs(pre, verbose=False, **kwargs)
    suc_configs = to_configs(suc, verbose=False, **kwargs)
    
    results = []
    if check_states:
        for pre_c, suc_c, pre_validation, suc_validation in zip(pre_configs, suc_configs, pre_validation, suc_validation):

            if pre_validation and suc_validation:
                succs = successors(pre_c)
                results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
            else:
                results.append(False)
    else:
        for pre_c, suc_c in zip(pre_configs, suc_configs):
            succs = successors(pre_c)
            results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
    return results 

Example 11

def validate_transitions(transitions, check_states=True, **kwargs):
    pre = np.array(transitions[0])
    suc = np.array(transitions[1])

    if check_states:
        pre_validation = validate_states(pre, verbose=False, **kwargs)
        suc_validation = validate_states(suc, verbose=False, **kwargs)

    pre_configs = to_configs(pre, verbose=False, **kwargs)
    suc_configs = to_configs(suc, verbose=False, **kwargs)
    
    results = []
    if check_states:
        for pre_c, suc_c, pre_validation, suc_validation in zip(pre_configs, suc_configs, pre_validation, suc_validation):

            if pre_validation and suc_validation:
                succs = successors(pre_c)
                results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
            else:
                results.append(False)
    else:
        for pre_c, suc_c in zip(pre_configs, suc_configs):
            succs = successors(pre_c)
            results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
    return results 

Example 12

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) 

Example 13

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) 

Example 14

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) 

Example 15

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) 

Example 16

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) 

Example 17

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 documentation purposes, 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 are done.
        assert_(np.equal(np.datetime64('NaT'), None)) 

Example 18

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() 

Example 19

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) 

Example 20

def categorical_accuracy(y_true, y_pred, mask=True):
    '''
    categorical_accuracy adjusted for padding mask
    '''
    # if mask is not None:
    print y_true
    print y_pred
    eval_shape = (reduce(mul, y_true.shape[:-1]), y_true.shape[-1])
    print eval_shape
    y_true_ = np.reshape(y_true, eval_shape)
    y_pred_ = np.reshape(y_pred, eval_shape)
    flat_mask = np.flatten(mask)
    comped = np.equal(np.argmax(y_true_, axis=-1),
                      np.argmax(y_pred_, axis=-1))
    ## not sure how to do this in tensor flow
    good_entries = flat_mask.nonzero()[0]
    return np.mean(np.gather(comped, good_entries))

    # else:
    #     return K.mean(K.equal(K.argmax(y_true, axis=-1),
    #                           K.argmax(y_pred, axis=-1))) 

Example 21

def __estimate_entropy__(self):
        counts = self.feature_vector_counts #Counter(self.timeline_feature_vectors)
        #print counts
        #N = float(sum(counts.values()))
        N = float(len(self.timeline) + 1)
        max_H = np.log(float(len(list(filter(lambda x: x, counts)))))

        if np.equal(max_H, 0.0):
            return 0.0

        entropy = 0.0

        for key in counts.keys():
            if counts[key] > 0:
                key_probability = counts[key] / N
                entropy += -(key_probability * np.log(key_probability))

        entropy /= max_H

        #print u'N={0}, |counts|={3}, max_H={1}, entropy={2}, counter={4}'.format(N, max_H, entropy, len(counts), counts)
        return entropy 

Example 22

def _f_dice(a, b):
    """DICE between two segmentations.

    Args:
        a: [..., H, W], binary mask
        b: [..., H, W], binary mask

    Returns:
        dice: [...]
    """
    card_a = a.sum(axis=-1).sum(axis=-1)
    card_b = b.sum(axis=-1).sum(axis=-1)
    card_ab = (a * b).sum(axis=-1).sum(axis=-1)
    card_sum = card_a + card_b
    dice = 2 * card_ab / (card_sum + np.equal(card_sum, 0).astype('float32'))
    return dice 

Example 23

def test_accuracy():
    def cat_acc(y_pred, y_true):
        return np.expand_dims(np.equal(np.argmax(y_pred, axis=-1), np.argmax(y_true, axis=-1)), -1),

    objectives_test(objectives.accuracy,
                    cat_acc,
                    np_pred=[[0,0,.9], [0,.9,0], [.9,0,0]],
                    np_true=[[0,0,1], [0,0,1], [0,0,1]])

    def bi_acc(y_pred, y_true):
        return np.equal(np.round(y_pred), y_true)

    objectives_test(objectives.accuracy,
                    bi_acc,
                    np_pred=[[0], [0.6], [0.7]],
                    np_true=[[0], [1], [1]]) 

Example 24

def test_convolutional_embedding_encoder(config, out_data_shape, out_data_length, out_seq_len):
    conv_embed = sockeye.encoder.ConvolutionalEmbeddingEncoder(config)

    data_nd = mx.nd.random_normal(shape=(_BATCH_SIZE, _SEQ_LEN, _NUM_EMBED))

    data = mx.sym.Variable("data", shape=data_nd.shape)
    data_length = mx.sym.Variable("data_length", shape=_DATA_LENGTH_ND.shape)

    (encoded_data,
     encoded_data_length,
     encoded_seq_len) = conv_embed.encode(data=data, data_length=data_length, seq_len=_SEQ_LEN)

    exe = encoded_data.simple_bind(mx.cpu(), data=data_nd.shape)
    exe.forward(data=data_nd)
    assert exe.outputs[0].shape == out_data_shape

    exe = encoded_data_length.simple_bind(mx.cpu(), data_length=_DATA_LENGTH_ND.shape)
    exe.forward(data_length=_DATA_LENGTH_ND)
    assert np.equal(exe.outputs[0].asnumpy(), np.asarray(out_data_length)).all()

    assert encoded_seq_len == out_seq_len 

Example 25

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) 

Example 26

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) 

Example 27

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) 

Example 28

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) 

Example 29

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) 

Example 30

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 documentation purposes, 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 are done.
        assert_(np.equal(np.datetime64('NaT'), None)) 

Example 31

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() 

Example 32

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) 

Example 33

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 

Example 34

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 

Example 35

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)) 

Example 36

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) 

Example 37

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) 

Example 38

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) 

Example 39

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) 

Example 40

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) 

Example 41

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)) 

Example 42

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() 

Example 43

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() 

Example 44

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) 

Example 45

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']] 

Example 46

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 

Example 47

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 

Example 48

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) 

Example 49

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) 

Example 50

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) 
点赞

发表评论

电子邮件地址不会被公开。 必填项已用*标注