python类asarray()的实例源码

text.py 文件源码 项目:AVSR-Deep-Speech 作者: pandeydivesh15 项目源码 文件源码 阅读 40 收藏 0 点赞 0 评论 0
def sparse_tuple_from(sequences, dtype=np.int32):
    r"""Creates a sparse representention of ``sequences``.
    Args:

        * sequences: a list of lists of type dtype where each element is a sequence

    Returns a tuple with (indices, values, shape)
    """
    indices = []
    values = []

    for n, seq in enumerate(sequences):
        indices.extend(zip([n]*len(seq), range(len(seq))))
        values.extend(seq)

    indices = np.asarray(indices, dtype=np.int64)
    values = np.asarray(values, dtype=dtype)
    shape = np.asarray([len(sequences), indices.max(0)[1]+1], dtype=np.int64)

    return tf.SparseTensor(indices=indices, values=values, shape=shape)
nn1_stress_test.py 文件源码 项目:YellowFin_Pytorch 作者: JianGoForIt 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def pad_batch(mini_batch):
    mini_batch_size = len(mini_batch)
#     print mini_batch.shape
#     print mini_batch
    max_sent_len1 = int(np.max([len(x[0]) for x in mini_batch]))
    max_sent_len2 = int(np.max([len(x[1]) for x in mini_batch]))
#     print max_sent_len1, max_sent_len2
#     max_token_len = int(np.mean([len(val) for sublist in mini_batch for val in sublist]))
    main_matrix1 = np.zeros((mini_batch_size, max_sent_len1), dtype= np.int)
    main_matrix2 = np.zeros((mini_batch_size, max_sent_len2), dtype= np.int)
    for idx1, i in enumerate(mini_batch):
        for idx2, j in enumerate(i[0]):
            try:
                main_matrix1[i,j] = j
            except IndexError:
                pass
    for idx1, i in enumerate(mini_batch):
        for idx2, j in enumerate(i[1]):
            try:
                main_matrix2[i,j] = j
            except IndexError:
                pass
    main_matrix1_t = Variable(torch.from_numpy(main_matrix1))
    main_matrix2_t = Variable(torch.from_numpy(main_matrix2))
#     print main_matrix1_t.size()
#     print main_matrix2_t.size()
    return [main_matrix1_t, main_matrix2_t]
#     return [Variable(torch.cat((main_matrix1_t, main_matrix2_t), 0))

# def pad_batch(mini_batch):
# #     print mini_batch
# #     print type(mini_batch)
# #     print mini_batch.shape
# #     for i, _ in enumerate(mini_batch):
# #         print i, _
#     return [Variable(torch.from_numpy(np.asarray(_))) for _ in mini_batch[0]]
images2gif.py 文件源码 项目:RasterFairy 作者: Quasimondo 项目源码 文件源码 阅读 41 收藏 0 点赞 0 评论 0
def quantize_without_scipy(self, image):
        """" This function can be used if no scipy is availabe.
        It's 7 times slower though.
        """
        w,h = image.size
        px = np.asarray(image).copy()
        memo = {}
        for j in range(w):
            for i in range(h):
                key = (px[i,j,0],px[i,j,1],px[i,j,2])
                try:
                    val = memo[key]
                except KeyError:
                    val = self.convert(*key)
                    memo[key] = val
                px[i,j,0],px[i,j,1],px[i,j,2] = val
        return Image.fromarray(px).convert("RGB").quantize(palette=self.paletteImage())
pointclouds.py 文件源码 项目:pybot 作者: spillai 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def xyz_array_to_pointcloud2(points, stamp=None, frame_id=None):
    '''
    Create a sensor_msgs.PointCloud2 from an array
    of points.
    '''
    msg = PointCloud2()
    if stamp:
        msg.header.stamp = stamp
    if frame_id:
        msg.header.frame_id = frame_id
    if len(points.shape) == 3:
        msg.height = points.shape[1]
        msg.width = points.shape[0]
    else:
        msg.height = 1
        msg.width = len(points)
    msg.fields = [
        PointField('x', 0, PointField.FLOAT32, 1),
        PointField('y', 4, PointField.FLOAT32, 1),
        PointField('z', 8, PointField.FLOAT32, 1)]
    msg.is_bigendian = False
    msg.point_step = 12
    msg.row_step = 12*points.shape[0]
    msg.is_dense = int(np.isfinite(points).all())
    msg.data = np.asarray(points, np.float32).tostring()

    return msg
text.py 文件源码 项目:AVSR-Deep-Speech 作者: pandeydivesh15 项目源码 文件源码 阅读 64 收藏 0 点赞 0 评论 0
def text_to_char_array(original):
    r"""
    Given a Python string ``original``, remove unsupported characters, map characters
    to integers and return a numpy array representing the processed string.
    """
    # Create list of sentence's words w/spaces replaced by ''
    result = original.replace(" '", "") # TODO: Deal with this properly
    result = result.replace("'", "")    # TODO: Deal with this properly
    result = result.replace(' ', '  ')
    result = result.split(' ')

    # Tokenize words into letters adding in SPACE_TOKEN where required
    result = np.hstack([SPACE_TOKEN if xt == '' else list(xt) for xt in result])

    # Map characters into indicies
    result = np.asarray([SPACE_INDEX if xt == SPACE_TOKEN else ord(xt) - FIRST_INDEX for xt in result])

    # Add result to results
    return result
text_RHL.py 文件源码 项目:AVSR-Deep-Speech 作者: pandeydivesh15 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def text_to_char_array(original):
    r"""
    Given a Python string ``original``, remove unsupported characters, map characters
    to integers and return a numpy array representing the processed string.
    """
    # Create list of sentence's words w/spaces replaced by ''
    result = original.replace(" '", "") # TODO: Deal with this properly
    result = result.replace("'", "")    # TODO: Deal with this properly
    result = result.replace(' ', '  ')
    result = result.split(' ')

    # Tokenize words into letters adding in SPACE_TOKEN where required
    result = np.hstack([SPACE_TOKEN if xt == '' else list(xt) for xt in result])

    # Map characters into indicies
    result = np.asarray([SPACE_INDEX if xt == SPACE_TOKEN else (
        ord(xt) - FIRST_INDEX if ord(xt)>FIRST_INDEX else 27+int(xt)) for xt in result])
    # Add result to results
    return result
text_RHL.py 文件源码 项目:AVSR-Deep-Speech 作者: pandeydivesh15 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def sparse_tuple_from(sequences, dtype=np.int32):
    r"""Creates a sparse representention of ``sequences``.
    Args:

        * sequences: a list of lists of type dtype where each element is a sequence

    Returns a tuple with (indices, values, shape)
    """
    indices = []
    values = []

    for n, seq in enumerate(sequences):
        indices.extend(zip([n]*len(seq), range(len(seq))))
        values.extend(seq)

    indices = np.asarray(indices, dtype=np.int64)
    values = np.asarray(values, dtype=dtype)
    shape = np.asarray([len(sequences), indices.max(0)[1]+1], dtype=np.int64)

    return tf.SparseTensor(indices=indices, values=values, shape=shape)
training.py 文件源码 项目:treecat 作者: posterior 项目源码 文件源码 阅读 46 收藏 0 点赞 0 评论 0
def __init__(self, data, tree_prior, config):
        """Initialize a model with an empty subsample.

        Args:
            data: An [N, V]-shaped numpy array of real-valued data.
            tree_prior: A [K]-shaped numpy array of prior edge log odds, where
                K is the number of edges in the complete graph on V vertices.
            config: A global config dict.
        """
        assert isinstance(data, np.ndarray)
        data = np.asarray(data, np.float32)
        assert len(data.shape) == 2
        N, V = data.shape
        D = config['model_latent_dim']
        E = V - 1  # Number of edges in the tree.
        TreeTrainer.__init__(self, N, V, tree_prior, config)
        self._data = data
        self._latent = np.zeros([N, V, D], np.float32)

        # This is symmetric positive definite.
        self._vert_ss = np.zeros([V, D, D], np.float32)
        # This is arbitrary (not necessarily symmetric).
        self._edge_ss = np.zeros([E, D, D], np.float32)
        # This represents (count, mean, covariance).
        self._feat_ss = np.zeros([V, D, 1 + 1 + D], np.float32)
utils.py 文件源码 项目:spyking-circus 作者: spyking-circus 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def _num_samples(x):
    """Return number of samples in array-like x."""
    if hasattr(x, 'fit'):
        # Don't get num_samples from an ensembles length!
        raise TypeError('Expected sequence or array-like, got '
                        'estimator %s' % x)
    if not hasattr(x, '__len__') and not hasattr(x, 'shape'):
        if hasattr(x, '__array__'):
            x = np.asarray(x)
        else:
            raise TypeError("Expected sequence or array-like, got %s" %
                            type(x))
    if hasattr(x, 'shape'):
        if len(x.shape) == 0:
            raise TypeError("Singleton array %r cannot be considered"
                            " a valid collection." % x)
        return x.shape[0]
    else:
        return len(x)
a3c.py 文件源码 项目:human-rl 作者: gsastry 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def process_rollout(rollout, gamma, lambda_=1.0):
    """
given a rollout, compute its returns and the advantage
"""
    batch_si = np.asarray(rollout.states)
    batch_a = np.asarray(rollout.actions)
    rewards = np.asarray(rollout.rewards)
    vpred_t = np.asarray(rollout.values + [rollout.r])

    rewards_plus_v = np.asarray(rollout.rewards + [rollout.r])
    batch_r = discount(rewards_plus_v, gamma)[:-1]
    delta_t = rewards + gamma * vpred_t[1:] - vpred_t[:-1]
    # this formula for the advantage comes "Generalized Advantage Estimation":
    # https://arxiv.org/abs/1506.02438
    batch_adv = discount(delta_t, gamma * lambda_)

    features = rollout.features[0]
    return Batch(batch_si, batch_a, batch_adv, batch_r, rollout.terminal, features)
a3c.py 文件源码 项目:human-rl 作者: gsastry 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def process_rollout(rollout, gamma, lambda_=1.0):
    """
given a rollout, compute its returns and the advantage
"""
    batch_si = np.asarray(rollout.states)
    batch_a = np.asarray(rollout.actions)
    rewards = np.asarray(rollout.rewards)
    vpred_t = np.asarray(rollout.values + [rollout.r])

    rewards_plus_v = np.asarray(rollout.rewards + [rollout.r])
    batch_r = discount(rewards_plus_v, gamma)[:-1]
    delta_t = rewards + gamma * vpred_t[1:] - vpred_t[:-1]
    # this formula for the advantage comes "Generalized Advantage Estimation":
    # https://arxiv.org/abs/1506.02438
    batch_adv = discount(delta_t, gamma * lambda_)

    features = rollout.features[0]
    return Batch(batch_si, batch_a, batch_adv, batch_r, rollout.terminal, features)
inference.py 文件源码 项目:dl-classification 作者: matthieuo 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def has_tomatoes(self, im_path):
        # load the image
        im = Image.open(im_path)
        im = np.asarray(im, dtype=np.float32)
        im = self.prepare_image(im)

        # launch an inference with the image
        pred = self.sess.run(
            self.output_logits, feed_dict={
                self.img_feed: im.eval(
                    session=self.sess)})

        if np.argmax(pred) == 0:
            print("NOT a tomato ! (confidence : ", pred[0, 0], "%)")
        else:
            print("We have a tomato ! (confidence : ", pred[0, 1], "%)")
agent.py 文件源码 项目:snake_game 作者: wing3s 项目源码 文件源码 阅读 44 收藏 0 点赞 0 评论 0
def play(self, nb_rounds):
        img_saver = save_image()
        img_saver.next()

        game_cnt = it.count(1)
        for i in xrange(nb_rounds):
            game = self.game(width=self.width, height=self.height)
            screen, _ = game.next()
            img_saver.send(screen)
            frame_cnt = it.count()
            try:
                state = np.asarray([screen] * self.nb_frames)
                while True:
                    frame_cnt.next()
                    act_idx = np.argmax(
                        self.model.predict(state[np.newaxis]), axis=-1)[0]
                    screen, _ = game.send(self.actions[act_idx])
                    state = np.roll(state, 1, axis=0)
                    state[0] = screen
                    img_saver.send(screen)
            except StopIteration:
                print 'Saved %4i frames for game %3i' % (
                    frame_cnt.next(), game_cnt.next())
        img_saver.close()
visualization.py 文件源码 项目:detection-2016-nipsws 作者: imatge-upc 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def draw_sequences_test(step, action, qval, draw, region_image, background, path_testing_folder,
                        region_mask, image_name, save_boolean):
    aux = np.asarray(region_image, np.uint8)
    img_offset = (1000 * step, 70)
    footnote_offset = (1000 * step, 550)
    q_predictions_offset = (1000 * step, 500)
    mask_img_offset = (1000 * step, 700)
    img_for_paste = Image.fromarray(aux)
    background.paste(img_for_paste, img_offset)
    mask_img = Image.fromarray(255 * region_mask)
    background.paste(mask_img, mask_img_offset)
    footnote = 'action: ' + str(action)
    q_val_predictions_text = str(qval)
    draw.text(footnote_offset, footnote, (0, 0, 0), font=font)
    draw.text(q_predictions_offset, q_val_predictions_text, (0, 0, 0), font=font)
    file_name = path_testing_folder + image_name + '.png'
    if save_boolean == 1:
        background.save(file_name)
    return background
mask.py 文件源码 项目:alchemy 作者: voidrank 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def encode(obj):
    # return single RLE
    if len(obj.shape) == 2:
        mask = obj
        masks = np.array(np.asarray([mask]))
        masks = _masks_as_fortran_order(masks)
        rles = _mask.encode(masks)
        rle = rles[0]
        return rle
    # return RLEs
    elif len(obj.shape) == 3:
        masks = obj
        masks = _masks_as_fortran_order(masks)
        rles = _mask.encode(masks)
        return rles
    else:
        raise Exception("Not Implement")
test_traversal.py 文件源码 项目:scipy2017 作者: deeplycloudy 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def test_prune_from_top():
    d, traversal = get_four_level_data_traversal()
    reduced_storm_id = [1,]
    d = traversal.reduce_to_entities('storm_id', reduced_storm_id)
    reduced_stroke_id = np.asarray([])
    reduced_flash_id = np.asarray([])
    reduced_trig_id = np.asarray([])
    assert_equal(d['storm_id'], reduced_storm_id)
    assert_equal(d['flash_id'], reduced_flash_id)
    assert_equal(d['stroke_id'], reduced_stroke_id)
    assert_equal(d['trig_id'], reduced_trig_id)

    reduced_storm_id = [2,]
    d = traversal.reduce_to_entities('storm_id', reduced_storm_id)
    reduced_flash_id = [4,5,6,7,8]
    reduced_stroke_id = [13,14,15,19,20,23,46]
    reduced_trig_id = [18,19,20,22,23,25,26,30,31,32]    
    assert_equal(d['storm_id'].data, reduced_storm_id)
    assert_equal(d['flash_id'].data, reduced_flash_id)
    assert_equal(d['stroke_id'].data, reduced_stroke_id)
    assert_equal(d['trig_id'].data, reduced_trig_id)
gan.py 文件源码 项目:vae-npvc 作者: JeremyCCHsu 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def _validate(self, machine, n=10):
        N = n * n
        z = np.random.normal(0., 1., size=[n, self.arch['z_dim']])
        z = np.concatenate([z] * n, axis=1)
        z = np.reshape(z, [N, -1]).astype(np.float32)  # consecutive rows
        y = np.asarray(
            [[5,   0,  0 ],
             [9,   0,  0 ],
             [12,  0,  0 ],
             [17,  0,  0 ],
             [19,  0,  0 ],
             [161, 0,  0 ],
             [170, 0,  0 ],
             [170, 16, 0 ],
             [161, 9,  4 ],
             [19,  24, 50]],
            dtype=np.int64)
        y = np.concatenate([y] * n, axis=0)
        Z = tf.constant(z)
        Y = tf.constant(y)
        Xh = machine.generate(Z, Y) # 100, 64, 64, 3
        Xh = make_png_thumbnail(Xh, n)
        return Xh
email_spam.py 文件源码 项目:Python-Machine-Learning-By-Example 作者: PacktPublishing 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def get_likelihood(term_document_matrix, label_index, smoothing=0):
    """ Compute likelihood based on training samples
    Args:
        term_document_matrix (sparse matrix)
        label_index (grouped sample indices by class)
        smoothing (integer, additive Laplace smoothing parameter)
    Returns:
        dictionary, with class as key, corresponding conditional probability P(feature|class) vector as value
    """
    likelihood = {}
    for label, index in label_index.items():
        likelihood[label] = term_document_matrix[index, :].sum(axis=0) + smoothing
        likelihood[label] = np.asarray(likelihood[label])[0]
        total_count = likelihood[label].sum()
        likelihood[label] = likelihood[label] / float(total_count)
    return likelihood
core.py 文件源码 项目:dask_gdf 作者: gpuopenanalytics 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def reset_index(self):
        """Reset index to range based
        """
        dfs = self.to_delayed()
        sizes = np.asarray(compute(*map(delayed(len), dfs)))
        prefixes = np.zeros_like(sizes)
        prefixes[1:] = np.cumsum(sizes[:-1])

        @delayed
        def fix_index(df, startpos):
            return df.set_index(np.arange(start=startpos,
                                          stop=startpos + len(df),
                                          dtype=np.intp))

        outdfs = [fix_index(df, startpos)
                  for df, startpos in zip(dfs, prefixes)]
        return from_delayed(outdfs)
sampler.py 文件源码 项目:pycma 作者: CMA-ES 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def __init__(self, dimension,
                 constant_trace='None',
                 randn=np.random.randn,
                 quadratic=False,
                 **kwargs):
        try:
            self.dimension = len(dimension)
            standard_deviations = np.asarray(dimension)
        except TypeError:
            self.dimension = dimension
            standard_deviations = np.ones(dimension)
        assert self.dimension == len(standard_deviations)
        assert len(standard_deviations) == self.dimension

        self.C = standard_deviations**2
        "covariance matrix diagonal"
        self.constant_trace = constant_trace
        self.randn = randn
        self.quadratic = quadratic
        self.count_tell = 0
sampler.py 文件源码 项目:pycma 作者: CMA-ES 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def norm(self, x):
        """compute the Mahalanobis norm that is induced by the
        statistical model / sample distribution, specifically by
        covariance matrix ``C``. The expected Mahalanobis norm is
        about ``sqrt(dimension)``.

        Example
        -------
        >>> import cma, numpy as np
        >>> sm = cma.sampler.GaussFullSampler(np.ones(10))
        >>> x = np.random.randn(10)
        >>> d = sm.norm(x)

        `d` is the norm "in" the true sample distribution,
        sampled points have a typical distance of ``sqrt(2*sm.dim)``,
        where ``sm.dim`` is the dimension, and an expected distance of
        close to ``dim**0.5`` to the sample mean zero. In the example,
        `d` is the Euclidean distance, because C = I.
        """
        return sum(np.asarray(x)**2 / self.C)**0.5
fitness_functions.py 文件源码 项目:pycma 作者: CMA-ES 项目源码 文件源码 阅读 38 收藏 0 点赞 0 评论 0
def elli(self, x, rot=0, xoffset=0, cond=1e6, actuator_noise=0.0, both=False):
        """Ellipsoid test objective function"""
        x = np.asarray(x)
        if not isscalar(x[0]):  # parallel evaluation
            return [self.elli(xi, rot) for xi in x]  # could save 20% overall
        if rot:
            x = rotate(x)
        N = len(x)
        if actuator_noise:
            x = x + actuator_noise * np.random.randn(N)

        ftrue = sum(cond**(np.arange(N) / (N - 1.)) * (x + xoffset)**2) \
                if N > 1 else (x + xoffset)**2

        alpha = 0.49 + 1. / N
        beta = 1
        felli = np.random.rand(1)[0]**beta * ftrue * \
                max(1, (10.**9 / (ftrue + 1e-99))**(alpha * np.random.rand(1)[0]))
        # felli = ftrue + 1*np.random.randn(1)[0] / (1e-30 +
        #                                           np.abs(np.random.randn(1)[0]))**0
        if both:
            return (felli, ftrue)
        else:
            # return felli  # possibly noisy value
            return ftrue  # + np.random.randn()
fitness_transformations.py 文件源码 项目:pycma 作者: CMA-ES 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def __call__(self, *args, **kwargs):
        # late initialization if necessary
        try:
            if not self.__initialized:
                raise AttributeError
        except AttributeError:
            Function.initialize(self, None)
        # find the "right" callable
        callable_ = self.__callable
        if callable_ is None:
            for name in self.function_names_to_evaluate_first_found:
                try:
                    callable_ = getattr(self, name)
                    break
                except AttributeError:
                    pass
        # call with each vector
        if callable_ is not None:
            X, list_revert = utils.as_vector_list(args[0])
            self.evaluations += len(X)
            return list_revert([
                    callable_(np.asarray(x), *args[1:], **kwargs)
                    for x in X])
        else:
            self.evaluations += 1  # somewhat bound to fail
fitness_transformations.py 文件源码 项目:pycma 作者: CMA-ES 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def __init__(self, fitness_function, multipliers=None, zero=None):
        """
        :param fitness_function: a `callable` object
        :param multipliers: coordinate-wise multipliers.
        :param zero: defines a new zero in preimage space, that is,
            calling the `ScaleCoordinates` instance returns
            ``fitness_function(multipliers * (x - zero))``.

        For both arguments, ``multipliers`` and ``zero``, to fit in
        case the length of the given input, superfluous trailing
        elements are ignored or the last element is recycled.
        """
        ComposedFunction.__init__(self,
                [fitness_function, self.scale_and_offset])
        self.multiplier = multipliers
        if self.multiplier is not None:
            self.multiplier = np.asarray(self.multiplier, dtype=float)
        self.zero = zero
        if zero is not None:
            self.zero = np.asarray(zero, dtype=float)
bbobbenchmarks.py 文件源码 项目:pycma 作者: CMA-ES 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def monotoneTFosc(f):
    """Maps [-inf,inf] to [-inf,inf] with different constants
    for positive and negative part.

    """
    if np.isscalar(f):
        if f > 0.:
            f = np.log(f) / 0.1
            f = np.exp(f + 0.49 * (np.sin(f) + np.sin(0.79 * f))) ** 0.1
        elif f < 0.:
            f = np.log(-f) / 0.1
            f = -np.exp(f + 0.49 * (np.sin(0.55 * f) + np.sin(0.31 * f))) ** 0.1
        return f
    else:
        f = np.asarray(f)
        g = f.copy()
        idx = (f > 0)
        g[idx] = np.log(f[idx]) / 0.1
        g[idx] = np.exp(g[idx] + 0.49 * (np.sin(g[idx]) + np.sin(0.79 * g[idx])))**0.1
        idx = (f < 0)
        g[idx] = np.log(-f[idx]) / 0.1
        g[idx] = -np.exp(g[idx] + 0.49 * (np.sin(0.55 * g[idx]) + np.sin(0.31 * g[idx])))**0.1
        return g
cpm_utils.py 文件源码 项目:convolutional-pose-machines-tensorflow 作者: timctho 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def make_heatmaps_from_joints(input_size, heatmap_size, gaussian_variance, batch_joints):
    # Generate ground-truth heatmaps from ground-truth 2d joints
    scale_factor = input_size // heatmap_size
    batch_gt_heatmap_np = []
    for i in range(batch_joints.shape[0]):
        gt_heatmap_np = []
        invert_heatmap_np = np.ones(shape=(heatmap_size, heatmap_size))
        for j in range(batch_joints.shape[1]):
            cur_joint_heatmap = make_gaussian(heatmap_size,
                                              gaussian_variance,
                                              center=(batch_joints[i][j] // scale_factor))
            gt_heatmap_np.append(cur_joint_heatmap)
            invert_heatmap_np -= cur_joint_heatmap
        gt_heatmap_np.append(invert_heatmap_np)
        batch_gt_heatmap_np.append(gt_heatmap_np)
    batch_gt_heatmap_np = np.asarray(batch_gt_heatmap_np)
    batch_gt_heatmap_np = np.transpose(batch_gt_heatmap_np, (0, 2, 3, 1))

    return batch_gt_heatmap_np
QAWind.py 文件源码 项目:QUANTAXIS 作者: yutiansut 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def QA_fetch_get_stock_risk(name, startDate, endDate):
    try:
        from WindPy import w
    except:
        QA_util_log_info('No WindPY Module!')
    w.start()
    if(QA_util_date_valid(endDate) == False):
        QA_util_log_info("wrong date")
    else:
        data = w.wsd(name, "annualyeild_100w,annualyeild_24m,annualyeild_60m,\
                    annualstdevr_100w,annualstdevr_24m,annualstdevr_60m,beta_100w,\
                    beta_24m,beta_60m,avgreturn,avgreturny,stdevry,stdcof,\
                    risk_nonsysrisk1,r2,alpha2,beta,sharpe,treynor,jensen,jenseny,betadf",
                     startDate, endDate, "period=2;returnType=1;index=000001.SH;yield=1")
        if (data.ErrorCode == 0):
            QA_util_log_info("Connent to Wind successfully")
    return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
data_loader.py 文件源码 项目:segmentation_DLMI 作者: imatge-upc 项目源码 文件源码 阅读 38 收藏 0 点赞 0 评论 0
def load_channels(self,normalize = False):
        modalities = []
        modalities.append(nib.load(self.FLAIR_FILE))
        modalities.append(nib.load(self.T1_FILE))

        channels = np.zeros( modalities[0].shape + (2,), dtype=np.float32)

        for index_mod, mod in enumerate(modalities):
            if self.data_augmentation:
                channels[:, :, :, index_mod] = flip_plane(np.asarray(mod.dataobj))
            else:
                channels[:,:,:,index_mod] = np.asarray(mod.dataobj)

            if normalize:
                channels[:, :, :, index_mod] = normalize_image(channels[:,:,:,index_mod], mask = self.load_ROI_mask() )


        return channels
data_loader_test.py 文件源码 项目:segmentation_DLMI 作者: imatge-upc 项目源码 文件源码 阅读 38 收藏 0 点赞 0 评论 0
def load_channels(self,normalize = False):
        modalities = []
        modalities.append(nib.load(self.FLAIR_FILE))
        modalities.append(nib.load(self.T1_FILE))

        channels = np.zeros( modalities[0].shape + (2,), dtype=np.float32)

        for index_mod, mod in enumerate(modalities):
            if self.data_augmentation:
                channels[:, :, :, index_mod] = flip_plane(np.asarray(mod.dataobj))
            else:
                channels[:,:,:,index_mod] = np.asarray(mod.dataobj)

            if normalize:
                channels[:, :, :, index_mod] = normalize_image(channels[:,:,:,index_mod], mask = self.load_ROI_mask() )


        return channels
data_loader.py 文件源码 项目:segmentation_DLMI 作者: imatge-upc 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def load_channels(self):
        flair = nib.load(self.FLAIR_FILE)
        t1 = nib.load(self.T1_FILE)
        t1c = nib.load(self.T1c_FILE)
        t2 = nib.load(self.T2_FILE)
        to_int = lambda b: 1 if b else 0
        num_input_modalities = to_int(self.booleanFLAIR) + to_int(self.booleanT1) + to_int(self.booleanT1c) + to_int(
            self.booleanT2)

        channels = np.zeros((num_input_modalities,) + flair.shape, dtype=np.float32)

        channels[0] = np.asarray(flair.dataobj) if self.booleanFLAIR is True else None
        channels[to_int(self.booleanFLAIR)] = np.asarray(t1.dataobj) if self.booleanT1 is True else None
        channels[to_int(self.booleanFLAIR) + to_int(self.booleanT1)] = np.asarray(
            t1c.dataobj) if self.booleanT1c is True else None
        channels[to_int(self.booleanFLAIR) + to_int(self.booleanT1) + to_int(self.booleanT1c)] = np.asarray(
            t2.dataobj) if self.booleanT2 is True else None

        return channels


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