Python numpy.logical_xor方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
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) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def test_truth_table_logical(self):
        # 2, 3 and 4 serves as true values
        input1 = [0, 0, 3, 2]
        input2 = [0, 4, 0, 2]

        typecodes = (np.typecodes['AllFloat']
                     + np.typecodes['AllInteger']
                     + '?')     # boolean
        for dtype in map(np.dtype, typecodes):
            arg1 = np.asarray(input1, dtype=dtype)
            arg2 = np.asarray(input2, dtype=dtype)

            # OR
            out = [False, True, True, True]
            for func in (np.logical_or, np.maximum):
                assert_equal(func(arg1, arg2).astype(bool), out)
            # AND
            out = [False, False, False, True]
            for func in (np.logical_and, np.minimum):
                assert_equal(func(arg1, arg2).astype(bool), out)
            # XOR
            out = [False, True, True, False]
            for func in (np.logical_xor, np.not_equal):
                assert_equal(func(arg1, arg2).astype(bool), out) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def test_reduce(self):
        none = np.array([0, 0, 0, 0], bool)
        some = np.array([1, 0, 1, 1], bool)
        every = np.array([1, 1, 1, 1], bool)
        empty = np.array([], bool)

        arrs = [none, some, every, empty]

        for arr in arrs:
            assert_equal(np.logical_and.reduce(arr), all(arr))

        for arr in arrs:
            assert_equal(np.logical_or.reduce(arr), any(arr))

        for arr in arrs:
            assert_equal(np.logical_xor.reduce(arr), arr.sum() % 2 == 1) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def update(self, actions, board, layers, backdrop, things, the_plot):
    # Where are the laser bolts? Only bolts from the player kill a Marauder.
    bolts = np.logical_or.reduce([layers[c] for c in UPWARD_BOLT_CHARS], axis=0)
    hits = bolts & self.curtain                       # Any hits to Marauders?
    np.logical_xor(self.curtain, hits, self.curtain)  # If so, zap the marauder...
    the_plot.add_reward(np.sum(hits)*10)              # ...and supply a reward.
    # Save the identities of marauder-striking bolts in the Plot.
    the_plot['marauder_hitters'] = [chr(c) for c in board[hits]]

    # If no Marauders are left, or if any are sitting on row 10, end the game.
    if (not self.curtain.any()) or self.curtain[10, :].any():
      return the_plot.terminate_episode()  # i.e. return None.

    # We move faster if there are fewer Marauders. The odd divisor causes speed
    # jumps to align on the high sides of multiples of 8; so, speed increases as
    # the number of Marauders decreases to 32 (or 24 etc.), not 31 (or 23 etc.).
    if the_plot.frame % max(1, np.sum(self.curtain)//8.0000001): return
    # If any Marauder reaches either side of the screen, reverse horizontal
    # motion and advance vertically one row.
    if np.any(self.curtain[:, 0] | self.curtain[:, -1]):
      self._dx = -self._dx
      self.curtain[:] = np.roll(self.curtain, shift=1, axis=0)
    self.curtain[:] = np.roll(self.curtain, shift=self._dx, axis=1) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
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
            ]

        # 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) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def _compute_masks(self):
        """Receives the attenuation and delay peaks and computes a mask to be applied to the signal for source
        separation.

        """
        # compute masks for separation
        best_so_far = np.inf * np.ones_like(self.stft_ch0, dtype=float)

        for i in range(0, self.num_sources):
            mask_array = np.zeros_like(self.stft_ch0, dtype=bool)
            phase = np.exp(-1j * self.frequency_matrix * self.delay_peak[i])
            score = np.abs(self.atn_peak[i] * phase * self.stft_ch0 - self.stft_ch1) ** 2 / (1 + self.atn_peak[i] ** 2)
            mask = (score < best_so_far)
            mask_array[mask] = True
            background_mask = self.mask_type(np.array(mask_array))
            self.result_masks.append(background_mask)
            self.result_masks[0].mask = np.logical_xor(self.result_masks[i].mask, self.result_masks[0].mask)
            best_so_far[mask] = score[mask]

        # Compute first mask based on what the other masks left remaining
        self.result_masks[0].mask = np.logical_not(self.result_masks[0].mask)
        return self.result_masks 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def generate_stimulus_xor(stim_times, gen_burst, n_inputs=2):
    inp_states = np.random.randint(2, size=(n_inputs, np.size(stim_times)))
    inp_spikes = []

    for times in ma.masked_values(inp_states, 0) * stim_times:
        # for each input (neuron): generate spikes according to state (=1) and stimulus time-grid
        spikes = np.concatenate([t + gen_burst() for t in times.compressed()])

        # round to simulation precision
        spikes *= 10
        spikes = spikes.round() + 1.0
        spikes = spikes / 10.0

        inp_spikes.append(spikes)

    # astype(int) could be omitted, because False/True has the same semantics
    targets = np.logical_xor(*inp_states).astype(int)

    return inp_spikes, targets 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def augment_occlusion_mask(self, masks, verbose=False, min_trans = 0.2, max_trans=0.7, max_occl = 0.25,min_occl = 0.0):


        new_masks = np.zeros_like(masks,dtype=np.bool)
        occl_masks_batch = self.random_syn_masks[np.random.choice(len(self.random_syn_masks),len(masks))]
        for idx,mask in enumerate(masks):
            occl_mask = occl_masks_batch[idx]
            while True:
                trans_x = int(np.random.choice([-1,1])*(np.random.rand()*(max_trans-min_trans) + min_trans)*occl_mask.shape[0])
                trans_y = int(np.random.choice([-1,1])*(np.random.rand()*(max_trans-min_trans) + min_trans)*occl_mask.shape[1])
                M = np.float32([[1,0,trans_x],[0,1,trans_y]])

                transl_occl_mask = cv2.warpAffine(occl_mask,M,(occl_mask.shape[0],occl_mask.shape[1]))

                overlap_matrix = np.invert(mask.astype(np.bool)) * transl_occl_mask.astype(np.bool)
                overlap = len(overlap_matrix[overlap_matrix==True])/float(len(mask[mask==0]))

                if overlap < max_occl and overlap > min_occl:
                    new_masks[idx,...] = np.logical_xor(mask.astype(np.bool), overlap_matrix)
                    if verbose:
                        print('overlap is ', overlap)
                    break

        return new_masks 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def testBCast(self):
    shapes = [
        ([1, 3, 2], [1]),
        ([1, 3, 2], [2]),
        ([1, 3, 2], [3, 2]),
        ([1, 3, 2], [3, 1]),
        ([1, 3, 2], [1, 3, 2]),
        ([1, 3, 2], [2, 3, 1]),
        ([1, 3, 2], [2, 1, 1]),
        ([1, 3, 2], [1, 3, 1]),
        ([2, 1, 5], [2, 3, 1]),
        ([2, 0, 5], [2, 0, 1]),
        ([2, 3, 0], [2, 3, 1]),
    ]
    for (xs, ys) in shapes:
      x = np.random.randint(0, 2, np.prod(xs)).astype(np.bool).reshape(xs)
      y = np.random.randint(0, 2, np.prod(ys)).astype(np.bool).reshape(ys)
      for use_gpu in [True, False]:
        self._compareBinary(x, y, np.logical_and, tf.logical_and, use_gpu)
        self._compareBinary(x, y, np.logical_or, tf.logical_or, use_gpu)
        self._compareBinary(x, y, np.logical_xor, tf.logical_xor, use_gpu) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def last_x2(prn,start,len):
  idx = start + np.arange(len)
  idx_x2 = idx % 15345037

  idx_a = idx % 15345000
  hold = idx_a>=(15345000-1069)
  idx_x2_a = idx_x2.copy()
  idx_x2_a[hold] = 4091
  p_x2a = x2a[idx_x2_a % 4092]

  idx_b = idx % 15345000
  hold = idx_b>=(15345000-965)
  idx_x2_b = idx_x2.copy()
  idx_x2_b[hold] = 4092
  p_x2b = x2b[idx_x2_b % 4093]

  return np.logical_xor(p_x2a,p_x2b) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def convert_traversible_to_graph(traversible, ff_cost=1., fo_cost=1.,
                                 oo_cost=1., connectivity=4):
  assert(connectivity == 4 or connectivity == 8)

  sz_x = traversible.shape[1]
  sz_y = traversible.shape[0]
  g, nodes = generate_lattice(sz_x, sz_y)

  # Assign costs.
  edge_wts = g.new_edge_property('float')
  g.edge_properties['wts'] = edge_wts
  wts = np.ones(g.num_edges(), dtype=np.float32)
  edge_wts.get_array()[:] = wts

  if connectivity == 8:
    add_diagonal_edges(g, nodes, sz_x, sz_y, np.sqrt(2.))

  se = np.array([[int(e.source()), int(e.target())] for e in g.edges()])
  s_xy = nodes[se[:,0]]
  t_xy = nodes[se[:,1]]
  s_t = np.ravel_multi_index((s_xy[:,1], s_xy[:,0]), traversible.shape)
  t_t = np.ravel_multi_index((t_xy[:,1], t_xy[:,0]), traversible.shape)
  s_t = traversible.ravel()[s_t]
  t_t = traversible.ravel()[t_t]

  wts = np.zeros(g.num_edges(), dtype=np.float32)
  wts[np.logical_and(s_t == True, t_t == True)] = ff_cost
  wts[np.logical_and(s_t == False, t_t == False)] = oo_cost
  wts[np.logical_xor(s_t, t_t)] = fo_cost

  edge_wts = g.edge_properties['wts']
  for i, e in enumerate(g.edges()):
    edge_wts[e] = edge_wts[e] * wts[i]
  # d = edge_wts.get_array()*1.
  # edge_wts.get_array()[:] = d*wts 
  return g, nodes 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def encodeMask(M):
        """
        Encode binary mask M using run-length encoding.
        :param   M (bool 2D array)  : binary mask to encode
        :return: R (object RLE)     : run-length encoding of binary mask
        """
        [h, w] = M.shape
        M = M.flatten(order='F')
        N = len(M)
        counts_list = []
        pos = 0
        # counts
        counts_list.append(1)
        diffs = np.logical_xor(M[0:N-1], M[1:N])
        for diff in diffs:
            if diff:
                pos +=1
                counts_list.append(1)
            else:
                counts_list[pos] += 1
        # if array starts from 1. start with 0 counts for 0
        if M[0] == 1:
            counts_list = [0] + counts_list
        return {'size':      [h, w],
               'counts':    counts_list ,
               } 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def encodeMask(M):
    """
    Encode binary mask M using run-length encoding.
    :param   M (bool 2D array)  : binary mask to encode
    :return: R (object RLE)     : run-length encoding of binary mask
    """
    [h, w] = M.shape
    M = M.flatten(order='F')
    N = len(M)
    counts_list = []
    pos = 0
    # counts
    counts_list.append(1)
    diffs = np.logical_xor(M[0:N - 1], M[1:N])
    for diff in diffs:
        if diff:
            pos += 1
            counts_list.append(1)
        else:
            counts_list[pos] += 1
    # if array starts from 1. start with 0 counts for 0
    if M[0] == 1:
        counts_list = [0] + counts_list
    return {'size': [h, w],
            'counts': counts_list,
            } 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def test_logical_and_or_xor(self):
        assert_array_equal(self.t | self.t, self.t)
        assert_array_equal(self.f | self.f, self.f)
        assert_array_equal(self.t | self.f, self.t)
        assert_array_equal(self.f | self.t, self.t)
        np.logical_or(self.t, self.t, out=self.o)
        assert_array_equal(self.o, self.t)
        assert_array_equal(self.t & self.t, self.t)
        assert_array_equal(self.f & self.f, self.f)
        assert_array_equal(self.t & self.f, self.f)
        assert_array_equal(self.f & self.t, self.f)
        np.logical_and(self.t, self.t, out=self.o)
        assert_array_equal(self.o, self.t)
        assert_array_equal(self.t ^ self.t, self.f)
        assert_array_equal(self.f ^ self.f, self.f)
        assert_array_equal(self.t ^ self.f, self.t)
        assert_array_equal(self.f ^ self.t, self.t)
        np.logical_xor(self.t, self.t, out=self.o)
        assert_array_equal(self.o, self.f)

        assert_array_equal(self.nm & self.t, self.nm)
        assert_array_equal(self.im & self.f, False)
        assert_array_equal(self.nm & True, self.nm)
        assert_array_equal(self.im & False, self.f)
        assert_array_equal(self.nm | self.t, self.t)
        assert_array_equal(self.im | self.f, self.im)
        assert_array_equal(self.nm | True, self.t)
        assert_array_equal(self.im | False, self.im)
        assert_array_equal(self.nm ^ self.t, self.im)
        assert_array_equal(self.im ^ self.f, self.im)
        assert_array_equal(self.nm ^ True, self.im)
        assert_array_equal(self.im ^ False, self.im) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import logical_xor [as 别名]
def make_boundaries(label, thickness=None):
        """
        Input is an image label, output is a numpy array mask encoding the boundaries of the objects
        Extract pixels at the true boundary by dilation - erosion of label.
        Don't just pick the void label as it is not exclusive to the boundaries.
        """
        assert(thickness is not None)
        import skimage.morphology as skm
        void = 255
        mask = np.logical_and(label > 0, label != void)[0]
        selem = skm.disk(thickness)
        boundaries = np.logical_xor(skm.dilation(mask, selem),
                                    skm.erosion(mask, selem))
        return boundaries