Python numpy.greater() 使用实例

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Example 1

def eliminate_overlapping_locations(f, separation):
    """ Makes sure that no position is within `separation` from each other, by
    deleting one of the that are to close to each other.
    """
    separation = validate_tuple(separation, f.shape[1])
    assert np.greater(separation, 0).all()
    # Rescale positions, so that pairs are identified below a distance of 1.
    f = f / separation
    while True:
        duplicates = cKDTree(f, 30).query_pairs(1)
        if len(duplicates) == 0:
            break
        to_drop = []
        for pair in duplicates:
            to_drop.append(pair[1])
        f = np.delete(f, to_drop, 0)
    return f * separation 

Example 2

def peaks(spectra,frequency,number=3,thresh=0.01):
        """ Return the peaks from the Fourier transform
            Variables:
            number:     integer. number of peaks to print.
            thresh:     float. Threshhold intensity for printing.

            Returns: Energy (eV), Intensity (depends on type of spectra)
        """

        from scipy.signal import argrelextrema as pks
        # find all peak indices [idx], and remove those below thresh [jdx]
        idx = pks(np.abs(spectra),np.greater,order=3)
        jdx = np.where((np.abs(spectra[idx]) >= thresh))
        kdx = idx[0][jdx[0]] # indices of peaks matching criteria
        if number > len(kdx):
            number = len(kdx)
        print("First "+str(number)+" peaks (eV) found: ")
        for i in xrange(number):
            print("{0:.4f}".format(frequency[kdx][i]*27.2114),
                  "{0:.4f}".format(spectra[kdx][i])) 

Example 3

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 4

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 5

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 6

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 7

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 8

def build(self, input_shape):
        super().build(input_shape)

        self.mask = np.ones(self.W_shape)

        assert mask.shape[0] == mask.shape[1]
        filter_size = self.mask.shape[0]
        filter_center = filter_size / 2

        self.mask[math.ceil(filter_center):] = 0
        self.mask[math.floor(filter_center):, math.ceil(filter_center):] = 0

        if self.mono:
            if self.mask_type == 'A':
                self.mask[math.floor(filter_center), math.floor(filter_center)] = 0
        else:
            op = np.greater_equal if self.mask_type == 'A' else np.greater
            for i in range(self.n_channels):
                for j in range(self.n_channels):
                    if op(i, j):
                        self.mask[math.floor(filter_center), math.floor(filter_center), i::self.n_channels, j::self.n_channels] = 0

        self.mask = K.variable(self.mask) 

Example 9

def _reset(self):
        """Resets wait counter and cooldown counter.
        """
        if self.mode not in ['auto', 'min', 'max']:
            warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
                          'fallback to auto mode.' % (self.mode),
                          RuntimeWarning)
            self.mode = 'auto'
        if (self.mode == 'min' or
           (self.mode == 'auto' and 'acc' not in self.monitor)):
            self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
            self.best = np.Inf
        else:
            self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
            self.best = -np.Inf
        self.cooldown_counter = 0
        self.wait = 0
        self.lr_epsilon = self.min_lr * 1e-4 

Example 10

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 11

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 12

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 13

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 14

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 15

def image_series_summary(tag, imgs, max_timesteps=10):
    # take only 3 items from the minibatch
    imgs = imgs[:, :3]

    # assume img.shape == (T, batch_size, n_obj, H, W, C)
    # let's log only for 1st obj
    tf.cond(tf.equal(tf.rank(imgs), 6), lambda: imgs[:, :, 0], lambda: imgs)

    shape = (max_timesteps,) + tuple(imgs.get_shape()[1:])
    nt = tf.shape(imgs)[0]

    def pad():
        paddings = tf.concat(axis=0, values=([[0, max_timesteps - nt]], tf.zeros((len(shape) - 1, 2), tf.int32)))
        return tf.pad(imgs, paddings)

    imgs = tf.cond(tf.greater(nt, max_timesteps), lambda: imgs[:max_timesteps], pad)
    imgs.set_shape(shape)
    imgs = tf.squeeze(imgs)
    imgs = tf.unstack(imgs)

    # concatenate along the columns
    imgs = tf.concat(axis=2, values=imgs)
    tf.summary.image(tag, imgs) 

Example 16

def get_local_maxima(x, y):

    """
    This function ...
    :param x:
    :param y:
    :return:
    """

    m = argrelextrema(y, np.greater)[0].tolist()

    # Find the index of the absolute maximum (should also be included, is not for example when it is at the edge)
    index = np.argmax(y)
    if index not in m: m.append(index)

    x_maxima = [x[i] for i in m]
    y_maxima = [y[i] for i in m]

    return x_maxima, y_maxima

# ----------------------------------------------------------------- 

Example 17

def get_local_maxima(x, y):

    """
    This function ...
    :param x:
    :param y:
    :return:
    """

    m = argrelextrema(y, np.greater)[0].tolist()

    # Find the index of the absolute maximum (should also be included, is not for example when it is at the edge)
    index = np.argmax(y)
    if index not in m: m.append(index)

    x_maxima = [x[i] for i in m]
    y_maxima = [y[i] for i in m]

    return x_maxima, y_maxima

# ----------------------------------------------------------------- 

Example 18

def detect_peaks(hist, count=2):
    hist_copy = hist    
    peaks = len(argrelextrema(hist_copy, np.greater, mode="wrap")[0])
    sigma = log1p(peaks)
    print(peaks, sigma)
    while (peaks > count):
        new_hist = gaussian_filter(hist_copy, sigma=sigma)        
        peaks = len(argrelextrema(new_hist, np.greater, mode="wrap")[0])
        if peaks < count:
            peaks = count + 1
            sigma = sigma * 0.5
            continue
        hist_copy = new_hist
        sigma = log1p(peaks)
    print(peaks, sigma)
    return argrelextrema(hist_copy, np.greater, mode="wrap")[0] 

Example 19

def _reset(self):
	    """Resets wait counter and cooldown counter.
	    """
	    if self.mode not in ['auto', 'min', 'max']:
	      logging.warning('Learning Rate Plateau Reducing mode %s is unknown, '
	                      'fallback to auto mode.' % (self.mode))
	      self.mode = 'auto'
	    if (self.mode == 'min' or
	        (self.mode == 'auto' and 'acc' not in self.monitor)):
	      self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
	      self.best = np.Inf
	    else:
	      self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
	      self.best = -np.Inf
	    self.cooldown_counter = 0
	    self.wait = 0
	    self.lr_epsilon = self.min_lr * 1e-4 

Example 20

def preprocess_labels(label, number_slices):
    """Preprocess the labels to adapt them to the loss computation requirements
    Args:
    Label corresponding to the input image (W,H) numpy array
    Returns:
    Label ready to compute the loss (1,W,H,1)
    """
    labels = [[] for i in range(np.array(label).shape[0])]

    for j in range(np.array(label).shape[0]):
        if type(label) is not np.ndarray:
            for i in range(number_slices):
                labels[j].append(np.array(Image.open(label[0][i]), dtype=np.uint8))

    label = np.array(labels[0])
    label = label.transpose((1, 2, 0))
    max_mask = np.max(label) * 0.5
    label = np.greater(label, max_mask)
    label = np.expand_dims(label, axis=0)

    return label 

Example 21

def class_balanced_cross_entropy_loss(output, label):
    """Define the class balanced cross entropy loss to train the network
    Args:
    output: Output of the network
    label: Ground truth label
    Returns:
    Tensor that evaluates the loss

    """

    labels = tf.cast(tf.greater(label, 0.5), tf.float32)

    output_gt_zero = tf.cast(tf.greater_equal(output, 0), tf.float32)

    loss_val = tf.multiply(output, (labels - output_gt_zero)) - tf.log(
        1 + tf.exp(output - 2 * tf.multiply(output, output_gt_zero)))

    loss_pos = tf.reduce_sum(-tf.multiply(labels, loss_val))
    loss_neg = tf.reduce_sum(-tf.multiply(1.0 - labels, loss_val))

    final_loss = 0.931 * loss_pos + 0.069 * loss_neg

    return final_loss 

Example 22

def dice_coef_theoretical(y_pred, y_true):
    """Define the dice coefficient
        Args:
        y_pred: Prediction
        y_true: Ground truth Label
        Returns:
        Dice coefficient
        """

    y_true_f = tf.cast(tf.reshape(y_true, [-1]), tf.float32)

    y_pred_f = tf.nn.sigmoid(y_pred)
    y_pred_f = tf.cast(tf.greater(y_pred_f, 0.5), tf.float32)
    y_pred_f = tf.cast(tf.reshape(y_pred_f, [-1]), tf.float32)

    intersection = tf.reduce_sum(y_true_f * y_pred_f)
    union = tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f)
    dice = (2. * intersection) / (union + 0.00001)

    if (tf.reduce_sum(y_pred) == 0) and (tf.reduce_sum(y_true) == 0):
        dice = 1

    return dice 

Example 23

def preprocess_labels(label, number_slices):
    """Preprocess the labels to adapt them to the loss computation requirements
    Args:
    Label corresponding to the input image (W,H) numpy array
    Returns:
    Label ready to compute the loss (1,W,H,1)
    """
    labels = [[] for i in range(np.array(label).shape[0])]  
    
    for j in range(np.array(label).shape[0]):
        if type(label) is not np.ndarray:
            for i in range(number_slices):
                labels[j].append(np.array(Image.open(label[0][i]), dtype=np.uint8))
            
    label = np.array(labels[0])
    label = label.transpose((1,2,0))
    max_mask = np.max(label) * 0.5
    label = np.greater(label, max_mask)
    label = np.expand_dims(label, axis=0)

    return label 

Example 24

def class_balanced_cross_entropy_loss(output, label, results_liver):
    """Define the class balanced cross entropy loss to train the network
    Args:
    output: Output of the network
    label: Ground truth label
    Returns:
    Tensor that evaluates the loss
   
    """

    labels = tf.cast(tf.greater(label, 0.5), tf.float32)

    output_gt_zero = tf.cast(tf.greater_equal(output, 0), tf.float32)
    loss_val = tf.multiply(output, (labels - output_gt_zero)) - tf.log(
        1 + tf.exp(output - 2 * tf.multiply(output, output_gt_zero)))
    
    loss_pos = tf.reduce_sum(-tf.multiply(results_liver, tf.multiply(labels, loss_val)))
    loss_neg = tf.reduce_sum(-tf.multiply(results_liver, tf.multiply(1.0 - labels, loss_val)))
        
    final_loss = 0.1018*loss_neg + 0.8982*loss_pos
    return final_loss 

Example 25

def preprocess_labels(label):
    """Preprocess the labels to adapt them to the loss computation requirements
    Args:
    Label corresponding to the input image (W,H) numpy array
    Returns:
    Label ready to compute the loss (1,W,H,1)
    """
    labels = [[] for i in range(np.array(label).shape[0])]  
    
    for j in range(np.array(label).shape[0]):
        if type(label) is not np.ndarray:
            for i in range(3):
                aux = np.array(Image.open(label[j][i]), dtype=np.uint8)
                crop = aux[int(float(x_bb[j])):int((float(x_bb[j])+80)), int(float(y_bb[j])): int((float(y_bb[j])+80))]
                labels[j].append(crop)
            
    label = np.array(labels[0])
    label = label.transpose((1,2,0))
    label = label[:, :, ::-1]
    max_mask = np.max(label) * 0.5
    label = np.greater(label, max_mask)
    label = np.expand_dims(label, axis=0)

    return label 

Example 26

def iterate_until_button_press(buttons, game_state, text_ending_place, text_starting_place):
    # while a button was not clicked this method checks if mouse is in the button and if it is
    # changes its colour
    button_clicked = 0
    while button_clicked == 0:
        pygame.display.update()
        user_events = event.events()
        # the first button is the title which is unclickable, thus iterating from 1 to len(buttons)
        for num in range(1, len(buttons)):
            if np.all((np.less(text_starting_place[num] - config.menu_spacing, user_events["mouse_pos"]),
                       np.greater(text_ending_place[num] + config.menu_spacing, user_events["mouse_pos"]))):
                if user_events["clicked"]:
                    button_clicked = num
                else:
                    game_state.canvas.surface.blit(
                        buttons[num][1], text_starting_place[num])
            else:
                game_state.canvas.surface.blit(
                    buttons[num][0], text_starting_place[num])
        if user_events["closed"] or user_events["quit_to_main_menu"]:
            button_clicked = len(buttons)-1
    return button_clicked 

Example 27

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 28

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 29

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 30

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 31

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) 

Example 32

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
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

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

Example 33

def test_identity_equality_mismatch(self):
        a = np.array([np.nan], dtype=object)

        with warnings.catch_warnings():
            warnings.filterwarnings('always', '', FutureWarning)
            assert_warns(FutureWarning, np.equal, a, a)
            assert_warns(FutureWarning, np.not_equal, a, a)

        with warnings.catch_warnings():
            warnings.filterwarnings('error', '', FutureWarning)
            assert_raises(FutureWarning, np.equal, a, a)
            assert_raises(FutureWarning, np.not_equal, a, a)
            # And the other do not warn:
            with np.errstate(invalid='ignore'):
                np.less(a, a)
                np.greater(a, a)
                np.less_equal(a, a)
                np.greater_equal(a, a) 

Example 34

def test_minmax_func(self):
        # Tests minimum and maximum.
        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
        # max doesn't work if shaped
        xr = np.ravel(x)
        xmr = ravel(xm)
        # following are true because of careful selection of data
        assert_equal(max(xr), maximum(xmr))
        assert_equal(min(xr), minimum(xmr))

        assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
        assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
        x = arange(5)
        y = arange(5) - 2
        x[3] = masked
        y[0] = masked
        assert_equal(minimum(x, y), where(less(x, y), x, y))
        assert_equal(maximum(x, y), where(greater(x, y), x, y))
        assert_(minimum(x) == 0)
        assert_(maximum(x) == 4)

        x = arange(4).reshape(2, 2)
        x[-1, -1] = masked
        assert_equal(maximum(x), 2) 

Example 35

def validate(self, mb_inputs, mb_targets, mb_probs):
    """"""
    
    sents = []
    mb_parse_probs, mb_rel_probs = mb_probs
    for inputs, targets, parse_probs, rel_probs in zip(mb_inputs, mb_targets, mb_parse_probs, mb_rel_probs):
      tokens_to_keep = np.greater(inputs[:,0], Vocab.ROOT)
      length = np.sum(tokens_to_keep)
      parse_preds, rel_preds = self.prob_argmax(parse_probs, rel_probs, tokens_to_keep)
      
      sent = -np.ones( (length, 9), dtype=int)
      tokens = np.arange(1, length+1)
      sent[:,0] = tokens
      sent[:,1:4] = inputs[tokens]
      sent[:,4] = targets[tokens,0]
      sent[:,5] = parse_preds[tokens]
      sent[:,6] = rel_preds[tokens]
      sent[:,7:] = targets[tokens, 1:]
      sents.append(sent)
    return sents
  
  #============================================================= 

Example 36

def validate(self, mb_inputs, mb_targets, mb_probs):
    """"""
    
    sents = []
    mb_parse_probs, mb_rel_probs = mb_probs
    for inputs, targets, parse_probs, rel_probs in zip(mb_inputs, mb_targets, mb_parse_probs, mb_rel_probs):
      tokens_to_keep = np.greater(inputs[:,0], Vocab.ROOT)
      length = np.sum(tokens_to_keep)
      parse_preds, rel_preds = self.prob_argmax(parse_probs, rel_probs, tokens_to_keep)
      
      sent = -np.ones( (length, 9), dtype=int)
      tokens = np.arange(1, length+1)
      sent[:,0] = tokens
      sent[:,1:4] = inputs[tokens]
      sent[:,4] = targets[tokens,0]
      sent[:,5] = parse_preds[tokens]
      sent[:,6] = rel_preds[tokens]
      sent[:,7:] = targets[tokens, 1:]
      sents.append(sent)
    return sents
  
  #============================================================= 

Example 37

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 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_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
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

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

Example 42

def test_minmax_func(self):
        # Tests minimum and maximum.
        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
        # max doesn't work if shaped
        xr = np.ravel(x)
        xmr = ravel(xm)
        # following are true because of careful selection of data
        assert_equal(max(xr), maximum(xmr))
        assert_equal(min(xr), minimum(xmr))

        assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
        assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
        x = arange(5)
        y = arange(5) - 2
        x[3] = masked
        y[0] = masked
        assert_equal(minimum(x, y), where(less(x, y), x, y))
        assert_equal(maximum(x, y), where(greater(x, y), x, y))
        assert_(minimum(x) == 0)
        assert_(maximum(x) == 4)

        x = arange(4).reshape(2, 2)
        x[-1, -1] = masked
        assert_equal(maximum(x), 2) 

Example 43

def preprocess_labels(label):
    """Preprocess the labels to adapt them to the loss computation requirements
    Args:
    Label corresponding to the input image (W,H) numpy array
    Returns:
    Label ready to compute the loss (1,W,H,1)
    """
    if type(label) is not np.ndarray:
        label = np.array(Image.open(label).split()[0], dtype=np.uint8)
    max_mask = np.max(label) * 0.5
    label = np.greater(label, max_mask)
    label = np.expand_dims(np.expand_dims(label, axis=0), axis=3)
    # label = tf.cast(np.array(label), tf.float32)
    # max_mask = tf.multiply(tf.reduce_max(label), 0.5)
    # label = tf.cast(tf.greater(label, max_mask), tf.float32)
    # label = tf.expand_dims(tf.expand_dims(label, 0), 3)
    return label 

Example 44

def class_balanced_cross_entropy_loss(output, label):
    """Define the class balanced cross entropy loss to train the network
    Args:
    output: Output of the network
    label: Ground truth label
    Returns:
    Tensor that evaluates the loss
    """

    labels = tf.cast(tf.greater(label, 0.5), tf.float32)

    num_labels_pos = tf.reduce_sum(labels)
    num_labels_neg = tf.reduce_sum(1.0 - labels)
    num_total = num_labels_pos + num_labels_neg

    output_gt_zero = tf.cast(tf.greater_equal(output, 0), tf.float32)
    loss_val = tf.multiply(output, (labels - output_gt_zero)) - tf.log(
        1 + tf.exp(output - 2 * tf.multiply(output, output_gt_zero)))

    loss_pos = tf.reduce_sum(-tf.multiply(labels, loss_val))
    loss_neg = tf.reduce_sum(-tf.multiply(1.0 - labels, loss_val))

    final_loss = num_labels_neg / num_total * loss_pos + num_labels_pos / num_total * loss_neg

    return final_loss 

Example 45

def class_balanced_cross_entropy_loss_theoretical(output, label):
    """Theoretical version of the class balanced cross entropy loss to train the network (Produces unstable results)
    Args:
    output: Output of the network
    label: Ground truth label
    Returns:
    Tensor that evaluates the loss
    """
    output = tf.nn.sigmoid(output)

    labels_pos = tf.cast(tf.greater(label, 0), tf.float32)
    labels_neg = tf.cast(tf.less(label, 1), tf.float32)

    num_labels_pos = tf.reduce_sum(labels_pos)
    num_labels_neg = tf.reduce_sum(labels_neg)
    num_total = num_labels_pos + num_labels_neg

    loss_pos = tf.reduce_sum(tf.multiply(labels_pos, tf.log(output + 0.00001)))
    loss_neg = tf.reduce_sum(tf.multiply(labels_neg, tf.log(1 - output + 0.00001)))

    final_loss = -num_labels_neg / num_total * loss_pos - num_labels_pos / num_total * loss_neg

    return final_loss 

Example 46

def __init__(self, monitor='val_loss', patience=0, verbose=0, mode='auto'):
        super(Callback, self).__init__()

        self.monitor = monitor
        self.patience = patience
        self.verbose = verbose
        self.wait = 0
        self.best_epoch = 0

        if mode == 'min':
            self.monitor_op = np.less
            self.best = np.Inf
        elif mode == 'max':
            self.monitor_op = np.greater
            self.best = -np.Inf
        else:
            if 'acc' in self.monitor:
                self.monitor_op = np.greater
                self.best = -np.Inf
            else:
                self.monitor_op = np.less
                self.best = np.Inf 

Example 47

def handle_rolling(agg, granularity, timestamps, values, is_aggregated,
                   references, window):
    if window > len(values):
        raise exceptions.UnAggregableTimeseries(
            references,
            "Rolling window '%d' is greater than serie length '%d'" %
            (window, len(values))
        )

    timestamps = timestamps[window - 1:]
    values = values.T
    # rigtorp.se/2011/01/01/rolling-statistics-numpy.html
    shape = values.shape[:-1] + (values.shape[-1] - window + 1, window)
    strides = values.strides + (values.strides[-1],)
    new_values = AGG_MAP[agg](as_strided(values, shape=shape, strides=strides),
                              axis=-1)
    return granularity, timestamps, new_values.T, is_aggregated 

Example 48

def infill_lines(idxs, idx_max):
    if len(idxs) == 0: 
        return np.array([])
    ends = np.array([i[[0,-1]] for i in idxs])
    ends = np.roll(ends.reshape(-1), -1).reshape(-1,2)
    
    if np.greater(*ends[-1]):
        ends[-1][1] += idx_max

    infill  = np.diff(ends, axis=1).reshape(-1) > 0
    aranges = ends[infill]
    if len(aranges) == 0:
        return np.array([])
    result  = np.vstack([pair_space(*i) for i in aranges])
    result %= idx_max
    return result 

Example 49

def __init__(self, monitor='val_loss', mode='auto', verbose=0):
        super(BestWeight, self).__init__()
        self.monitor = monitor
        self.mode = mode
        self.best_weights = None
        self.verbose = verbose
        if mode == 'min':
            self.monitor_op = np.less
            self.best = np.Inf
        elif mode == 'max':
            self.monitor_op = np.greater
            self.best = -np.Inf
        else:
            if 'acc' in self.monitor:
                self.monitor_op = np.greater
                self.best = -np.Inf
            else:
                self.monitor_op = np.less
                self.best = np.Inf 

Example 50

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) 
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