python类choose()的实例源码

def compress_image(img, num_clusters):
    # Convert input image into (num_samples, num_features) 
    # array to run kmeans clustering algorithm 
    X = img.reshape((-1, 1))  

    # Run kmeans on input data
    kmeans = cluster.KMeans(n_clusters=num_clusters, n_init=4, random_state=5)
    kmeans.fit(X)
    centroids = kmeans.cluster_centers_.squeeze()
    labels = kmeans.labels_

    # Assign each value to the nearest centroid and 
    # reshape it to the original image shape
    input_image_compressed = np.choose(labels, centroids).reshape(img.shape)

    return input_image_compressed

def hsv2rgb(hsv):
    hsv = np.array(hsv)
    input_shape = hsv.shape
    hsv = hsv.reshape(-1, 3)
    h, s, v = hsv[:, 0] / 255, hsv[:, 1] / 255, hsv[:, 2]

    i = np.uint32(h * 6.0)  # pylint: disable=no-member
    f = (h * 6.0) - i
    p = v * (1.0 - s)
    q = v * (1.0 - s * f)
    t = v * (1.0 - s * (1.0 - f))
    i = i % 6

    rgb = np.zeros_like(hsv, np.uint8)
    v, t, p, q = v.reshape(-1, 1), t.reshape(-1, 1), p.reshape(-1, 1), q.reshape(-1, 1)
    # This could probably be much faster if replaced with np.choose
    rgb[i == 0] = np.hstack([v, t, p])[i == 0]
    rgb[i == 1] = np.hstack([q, v, p])[i == 1]
    rgb[i == 2] = np.hstack([p, v, t])[i == 2]
    rgb[i == 3] = np.hstack([p, q, v])[i == 3]
    rgb[i == 4] = np.hstack([t, p, v])[i == 4]
    rgb[i == 5] = np.hstack([v, p, q])[i == 5]
    rgb[s == 0.0] = np.hstack([v, v, v])[s == 0.0]

    return rgb.reshape(input_shape)

def find_ray_grids(self, coord, axis):
        """
        Returns the (objects, indices) of grids that an (x,y) ray intersects
        along *axis*
        """
        # Let's figure out which grids are on the slice
        mask=np.ones(self.num_grids)
        # So if gRE > coord, we get a mask, if not, we get a zero
        #    if gLE > coord, we get a zero, if not, mask
        # Thus, if the coordinate is between the two edges, we win!
        xax = self.ds.coordinates.x_axis[axis]
        yax = self.ds.coordinates.y_axis[axis]
        np.choose(np.greater(self.grid_right_edge[:,xax],coord[0]),(0,mask),mask)
        np.choose(np.greater(self.grid_left_edge[:,xax],coord[0]),(mask,0),mask)
        np.choose(np.greater(self.grid_right_edge[:,yax],coord[1]),(0,mask),mask)
        np.choose(np.greater(self.grid_left_edge[:,yax],coord[1]),(mask,0),mask)
        ind = np.where(mask == 1)
        return self.grids[ind], ind

def forward(self, inputs):
        x, t = inputs
        if chainer.is_debug():
            if not ((0 <= t).all() and
                    (t < x.shape[1]).all()):
                msg = 'Each label `t` need to satisfty `0 <= t < x.shape[1]`'
                raise ValueError(msg)

        xp = cuda.get_array_module(x)
        if xp is numpy:
            # This code is equivalent to `t.choose(x.T)`, but `numpy.choose`
            # does not work when `x.shape[1] > 32`.
            return x[six.moves.range(t.size), t],
        else:
            y = cuda.elementwise(
                'S t, raw T x',
                'T y',
                'int ind[] = {i, t}; y = x[ind];',
                'getitem_fwd'
            )(t, x)
            return y,

def raster_copy_with_nodata( s_fh, s_xoff, s_yoff, s_xsize, s_ysize, s_band_n,
                             t_fh, t_xoff, t_yoff, t_xsize, t_ysize, t_band_n,
                             nodata ):
    try:
        import numpy as Numeric
    except ImportError:
        import Numeric

    s_band = s_fh.GetRasterBand( s_band_n )
    t_band = t_fh.GetRasterBand( t_band_n )

    data_src = s_band.ReadAsArray( s_xoff, s_yoff, s_xsize, s_ysize,
                                   t_xsize, t_ysize )
    data_dst = t_band.ReadAsArray( t_xoff, t_yoff, t_xsize, t_ysize )

    nodata_test = Numeric.equal(data_src,nodata)
    to_write = Numeric.choose( nodata_test, (data_src, data_dst) )

    t_band.WriteArray( to_write, t_xoff, t_yoff )

    return 0

# =============================================================================

def raster_copy_with_mask( s_fh, s_xoff, s_yoff, s_xsize, s_ysize, s_band_n,
                           t_fh, t_xoff, t_yoff, t_xsize, t_ysize, t_band_n,
                           m_band ):
    try:
        import numpy as Numeric
    except ImportError:
        import Numeric

    s_band = s_fh.GetRasterBand( s_band_n )
    t_band = t_fh.GetRasterBand( t_band_n )

    data_src = s_band.ReadAsArray( s_xoff, s_yoff, s_xsize, s_ysize,
                                   t_xsize, t_ysize )
    data_mask = m_band.ReadAsArray( s_xoff, s_yoff, s_xsize, s_ysize,
                                    t_xsize, t_ysize )
    data_dst = t_band.ReadAsArray( t_xoff, t_yoff, t_xsize, t_ysize )

    mask_test = Numeric.equal(data_mask, 0)
    to_write = Numeric.choose( mask_test, (data_src, data_dst) )

    t_band.WriteArray( to_write, t_xoff, t_yoff )

    return 0

# =============================================================================

def test_broadcasted(self):
        a = tensor.scalar(dtype='int32')
        b = tensor.matrix(dtype='float32')

        # Test when a is broadcastable
        A = 3
        B = numpy.asarray(numpy.random.rand(4, 4), dtype='float32')

        for m in self.modes:
            f = function([a, b], choose(a, b, mode=m))
            t_c = f(A, B)
            n_c = numpy.choose(A, B, mode=m)
            assert numpy.allclose(t_c, n_c)

        # Test when the result should be broadcastable
        b = theano.tensor.col(dtype='float32')
        B = numpy.asarray(numpy.random.rand(4, 1), dtype='float32')
        for m in self.modes:
            f = function([a, b], choose(a, b, mode=m))
            assert choose(a, b, mode=m).broadcastable[0]
            t_c = f(A, B)
            n_c = numpy.choose(A, B, mode=m)
            assert numpy.allclose(t_c, n_c)

def ___test_infer_shape_tuple(self):

        a = tensor.tensor3(dtype='int32')
        b = tensor.tensor3(dtype='int32')
        c = tensor.tensor3(dtype='int32')

        A = numpy.asarray([1, 0], dtype='int32').reshape((2, 1, 1))
        B = numpy.asarray(numpy.random.rand(1, 4, 1), dtype='int32')
        C = numpy.asarray(numpy.random.rand(1, 1, 7), dtype='int32')

        f = function([a, b, c], choose(a, (b, c)))
        shape = (2, 4, 7)
        assert numpy.allclose(f(A, B, C).shape, shape)

        self._compile_and_check([a, b, c],  # theano.function inputs
                                [self.op(a, (b, c))],  # theano.function outputs
                                # Always use not square matrix!
                                # inputs data
                                [A, B, C],
                                # Op that should be removed from the graph.
                                self.op_class)

def th_external_dG(self, seqs):
        # Make a boolean vector representing which toeholds' external 3' context dG
        # is further from the target dG than than their external 5' context dG
        dG_external3 = self.th_external_3_dG(seqs)
        dG_external5 = self.th_external_5_dG(seqs)
        external_further_bool = np.abs(dG_external3 - self.targetdG) >\
                                np.abs(dG_external5 - self.targetdG)
        return np.choose(external_further_bool, [dG_external5, dG_external3])

def matching_uniform(self, seqs):
        # Make a boolean vector representing which toeholds' external context dG
        # is further from the target dG than than their internal context dG
        dG_external = self.th_external_dG(seqs)
        dG_internal = self.th_internal_dG(seqs)
        external_further_bool = np.abs(dG_external - self.targetdG) >\
                                np.abs(dG_internal - self.targetdG)
        return np.choose(external_further_bool, [dG_internal, dG_external])

def matching_uniform(self, seqs):
        # Make a boolean vector representing which toeholds' external context dG
        # is further from the target dG than than their internal context dG
        dG_external = self.th_external_dG(seqs)
        dG_internal = self.th_internal_dG(seqs)
        external_further_bool = np.abs(dG_external - self.targetdG) >\
                                np.abs(dG_internal - self.targetdG)
        return np.choose(external_further_bool, [dG_internal, dG_external])

def _get_hidden_layer_connectivity(self, layerIdx):
        layer_size = self._hidden_sizes[layerIdx]
        if layerIdx == 0:
            p_vals = self._get_p(T.min(self.layers_connectivity[layerIdx]))
        else:
            p_vals = self._get_p(T.min(self.layers_connectivity_updates[layerIdx-1]))

        # #Implementations of np.choose in theano GPU
        # return T.nonzero(self._mrng.multinomial(pvals=[self._p_vals] * layer_size, dtype=theano.config.floatX))[1].astype(dtype=theano.config.floatX)
        # return T.argmax(self._mrng.multinomial(pvals=[self._p_vals] * layer_size, dtype=theano.config.floatX), axis=1)
        return T.sum(T.cumsum(self._mrng.multinomial(pvals=T.tile(p_vals[::-1][None, :], (layer_size, 1)), dtype=theano.config.floatX), axis=1), axis=1)

def test_choose(self):
        choices = [[0, 1, 2],
                   [3, 4, 5],
                   [5, 6, 7]]
        tgt = [5, 1, 5]
        a = [2, 0, 1]

        out = np.choose(a, choices)
        assert_equal(out, tgt)

def clip(self, a, m, M, out=None):
        # use slow-clip
        selector = np.less(a, m) + 2*np.greater(a, M)
        return selector.choose((a, m, M), out=out)

    # Handy functions

def test_mixed(self):
        c = np.array([True, True])
        a = np.array([True, True])
        assert_equal(np.choose(c, (a, 1)), np.array([1, 1]))

def test_choose(self):
        x = 2*np.ones((3,), dtype=int)
        y = 3*np.ones((3,), dtype=int)
        x2 = 2*np.ones((2, 3), dtype=int)
        y2 = 3*np.ones((2, 3), dtype=int)
        ind = np.array([0, 0, 1])

        A = ind.choose((x, y))
        assert_equal(A, [2, 2, 3])

        A = ind.choose((x2, y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

        A = ind.choose((x, y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def test_all(self):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        for i in range(a.ndim):
            amax = a.max(i)
            aargmax = a.argmax(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(amax == aargmax.choose(*a.transpose(i,*axes))))

def test_basic(self):
        A = np.choose(self.ind, (self.x, self.y))
        assert_equal(A, [2, 2, 3])

def test_broadcast1(self):
        A = np.choose(self.ind, (self.x2, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def test_broadcast2(self):
        A = np.choose(self.ind, (self.x, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def _next_frame(self):
        sources = np.random.randint(0, 6, len(self.my_pixels))
        colors = np.array([RED, BLUE, GREEN, BLACK, BLACK, BLACK])
        for i in range(3):
            c = np.choose(sources, colors[:, i])
            self.my_pixels[:, i] = c

def _next_frame(self):
        sources = np.random.randint(0, 7, len(self.my_pixels))
        colors = np.concatenate((self.colors, [BLACK, BLACK, BLACK]))
        for i in range(3):
            c = np.choose(sources, colors[:, i])
            self.my_pixels[:, i] = c

def _next_frame(self):
        self.colors[:, 0] = self.colors[:, 0] + 119
        self.sources = np.random.randint(0, 7, len(self.my_pixels))
        colors = np.concatenate((self.colors, [BLACK, BLACK, BLACK]))
        for i in range(3):
            c = np.choose(self.sources, colors[:, i])
            self.my_pixels[:, i] = c

def test_choose(self):
        choices = [[0, 1, 2],
                   [3, 4, 5],
                   [5, 6, 7]]
        tgt = [5, 1, 5]
        a = [2, 0, 1]

        out = np.choose(a, choices)
        assert_equal(out, tgt)

def clip(self, a, m, M, out=None):
        # use slow-clip
        selector = np.less(a, m) + 2*np.greater(a, M)
        return selector.choose((a, m, M), out=out)

    # Handy functions

def test_mixed(self):
        c = np.array([True, True])
        a = np.array([True, True])
        assert_equal(np.choose(c, (a, 1)), np.array([1, 1]))

def test_choose(self):
        x = 2*np.ones((3,), dtype=int)
        y = 3*np.ones((3,), dtype=int)
        x2 = 2*np.ones((2, 3), dtype=int)
        y2 = 3*np.ones((2, 3), dtype=int)
        ind = np.array([0, 0, 1])

        A = ind.choose((x, y))
        assert_equal(A, [2, 2, 3])

        A = ind.choose((x2, y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

        A = ind.choose((x, y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def test_all(self):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        for i in range(a.ndim):
            amax = a.max(i)
            aargmax = a.argmax(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(amax == aargmax.choose(*a.transpose(i,*axes))))

def test_basic(self):
        A = np.choose(self.ind, (self.x, self.y))
        assert_equal(A, [2, 2, 3])

def test_broadcast1(self):
        A = np.choose(self.ind, (self.x2, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])