Python numpy.int方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def test_bitmap_mask_crop():
    # crop with empty bitmap masks
    dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int)
    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    cropped_masks = bitmap_masks.crop(dummy_bbox)
    assert len(cropped_masks) == 0
    assert cropped_masks.height == 17
    assert cropped_masks.width == 10

    # crop with bitmap masks contain 3 instances
    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    cropped_masks = bitmap_masks.crop(dummy_bbox)
    assert len(cropped_masks) == 3
    assert cropped_masks.height == 17
    assert cropped_masks.width == 10
    x1, y1, x2, y2 = dummy_bbox
    assert (cropped_masks.masks == raw_masks[:, y1:y2, x1:x2]).all()

    # crop with invalid bbox
    with pytest.raises(AssertionError):
        dummy_bbox = dummy_bboxes(2, 28, 28)
        bitmap_masks.crop(dummy_bbox) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def _project_im_rois(im_rois, scales):
    """Project image RoIs into the image pyramid built by _get_image_blob.
    Arguments:
        im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
        scales (list): scale factors as returned by _get_image_blob
    Returns:
        rois (ndarray): R x 4 matrix of projected RoI coordinates
        levels (list): image pyramid levels used by each projected RoI
    """
    im_rois = im_rois.astype(np.float, copy=False)

    if len(scales) > 1:
        widths = im_rois[:, 2] - im_rois[:, 0] + 1
        heights = im_rois[:, 3] - im_rois[:, 1] + 1
        areas = widths * heights
        scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2)
        diff_areas = np.abs(scaled_areas - 224 * 224)
        levels = diff_areas.argmin(axis=1)[:, np.newaxis]
    else:
        levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)

    rois = im_rois * scales[levels]

    return rois, levels 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def __getitem__(self, idx):
        """Get training/test data after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Training/test data (with annotation if `test_mode` is set
                True).
        """

        if self.test_mode:
            return self.prepare_test_img(idx)
        while True:
            data = self.prepare_train_img(idx)
            if data is None:
                idx = self._rand_another(idx)
                continue
            return data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def prepare_test_img(self, idx):
        """Get testing data  after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Testing data after pipeline with new keys intorduced by
                piepline.
        """

        img_info = self.data_infos[idx]
        results = dict(img_info=img_info)
        if self.proposals is not None:
            results['proposals'] = self.proposals[idx]
        self.pre_pipeline(results)
        return self.pipeline(results) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def to_image_spec(img, **kw):
    '''
    to_image_spec(img) yields a dictionary of meta-data for the given nibabel image object img.
    to_image_spec(hdr) yields the equivalent meta-data for the given nibabel image header.

    Note that obj may also be a mapping object, in which case it is returned verbatim.
    '''
    if pimms.is_vector(img,'int') and is_tuple(img) and len(img) < 5:
        r = image_array_to_spec(np.zeros(img))
    elif pimms.is_map(img):    r = img
    elif is_image_header(img): r = image_header_to_spec(img)
    elif is_image(img):        r = image_to_spec(img)
    elif is_image_array(img):  r = image_array_to_spec(img)
    else: raise ValueError('cannot convert object of type %s to image-spec' % type(img))
    if len(kw) > 0: r = {k:v for m in (r,kw) for (k,v) in six.iteritems(m)}
    # normalize the entries
    for (k,aliases) in six.iteritems(imspec_aliases):
        if k in r: continue
        for al in aliases:
            if al in r:
                val = r[al]
                r = pimms.assoc(pimms.dissoc(r, al), k, val)
                break
    return r 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def cleaned_visual_areas(visual_areas, faces):
        '''
        mdl.cleaned_visual_areas is the same as mdl.visual_areas except that vertices with visual
        area values of 0 (boundary values) are given the mode of their neighbors.
        '''
        area_ids = np.array(visual_areas)
        boundaryNeis = {}
        for (b,inside) in [(b, set(inside))
                           for t in faces.T
                           for (bound, inside) in [([i for i in t if area_ids[i] == 0],
                                                    [i for i in t if area_ids[i] != 0])]
                           if len(bound) > 0 and len(inside) > 0
                           for b in bound]:
            if b in boundaryNeis: boundaryNeis[b] |= inside
            else:                 boundaryNeis[b] =  inside
        for (b,neis) in six.iteritems(boundaryNeis):
            area_ids[b] = np.argmax(np.bincount(area_ids[list(neis)]))
        return pimms.imm_array(np.asarray(area_ids, dtype=np.int)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def curve_length(self, start=None, end=None, precision=0.01):
        '''
        Calculates the length of the curve by dividing the curve up
        into pieces of parameterized-length <precision>.
        '''
        if start is None: start = self.t[0]
        if end is None: end = self.t[-1]
        from scipy import interpolate
        if self.order == 1:
            # we just want to add up along the steps...
            ii = [ii for (ii,t) in enumerate(self.t) if start < t and t < end]
            ts = np.concatenate([[start], self.t[ii], [end]])
            xy = np.vstack([[self(start)], self.coordinates[:,ii].T, [self(end)]])
            return np.sum(np.sqrt(np.sum((xy[1:] - xy[:-1])**2, axis=1)))
        else:
            t = np.linspace(start, end, int(np.ceil((end-start)/precision)))
            dt = t[1] - t[0]
            dx = interpolate.splev(t, self.splrep[0], der=1)
            dy = interpolate.splev(t, self.splrep[1], der=1)
            return np.sum(np.sqrt(dx**2 + dy**2)) * dt 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def dqn_sym_nips(action_num, data=None, name='dqn'):
    """Structure of the Deep Q Network in the NIPS 2013 workshop paper:
    Playing Atari with Deep Reinforcement Learning (https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)

    Parameters
    ----------
    action_num : int
    data : mxnet.sym.Symbol, optional
    name : str, optional
    """
    if data is None:
        net = mx.symbol.Variable('data')
    else:
        net = data
    net = mx.symbol.Convolution(data=net, name='conv1', kernel=(8, 8), stride=(4, 4), num_filter=16)
    net = mx.symbol.Activation(data=net, name='relu1', act_type="relu")
    net = mx.symbol.Convolution(data=net, name='conv2', kernel=(4, 4), stride=(2, 2), num_filter=32)
    net = mx.symbol.Activation(data=net, name='relu2', act_type="relu")
    net = mx.symbol.Flatten(data=net)
    net = mx.symbol.FullyConnected(data=net, name='fc3', num_hidden=256)
    net = mx.symbol.Activation(data=net, name='relu3', act_type="relu")
    net = mx.symbol.FullyConnected(data=net, name='fc4', num_hidden=action_num)
    net = mx.symbol.Custom(data=net, name=name, op_type='DQNOutput')
    return net 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def _validate_csr_generation_inputs(num_rows, num_cols, density,
                                    distribution="uniform"):
    """Validates inputs for csr generation helper functions
    """
    total_nnz = int(num_rows * num_cols * density)
    if density < 0 or density > 1:
        raise ValueError("density has to be between 0 and 1")

    if num_rows <= 0 or num_cols <= 0:
        raise ValueError("num_rows or num_cols should be greater than 0")

    if distribution == "powerlaw":
        if total_nnz < 2 * num_rows:
            raise ValueError("not supported for this density: %s"
                             " for this shape (%s, %s)"
                             " Please keep :"
                             " num_rows * num_cols * density >= 2 * num_rows"
                             % (density, num_rows, num_cols)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def gen_buckets_probs_with_ppf(ppf, nbuckets):
    """Generate the buckets and probabilities for chi_square test when the ppf (Quantile function)
     is specified.

    Parameters
    ----------
    ppf : function
        The Quantile function that takes a probability and maps it back to a value.
        It's the inverse of the cdf function
    nbuckets : int
        size of the buckets

    Returns
    -------
    buckets : list of tuple
        The generated buckets
    probs : list
        The generate probabilities
    """
    assert nbuckets > 0
    probs = [1.0 / nbuckets for _ in range(nbuckets)]
    buckets = [(ppf(i / float(nbuckets)), ppf((i + 1) / float(nbuckets))) for i in range(nbuckets)]
    return buckets, probs 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def _project_to_map(map, vertex, wt=None, ignore_points_outside_map=False):
  """Projects points to map, returns how many points are present at each
  location."""
  num_points = np.zeros((map.size[1], map.size[0]))
  vertex_ = vertex[:, :2] - map.origin
  vertex_ = np.round(vertex_ / map.resolution).astype(np.int)
  if ignore_points_outside_map:
    good_ind = np.all(np.array([vertex_[:,1] >= 0, vertex_[:,1] < map.size[1],
                                vertex_[:,0] >= 0, vertex_[:,0] < map.size[0]]),
                      axis=0)
    vertex_ = vertex_[good_ind, :]
    if wt is not None:
      wt = wt[good_ind, :]
  if wt is None:
    np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), 1)
  else:
    assert(wt.shape[0] == vertex.shape[0]), \
      'number of weights should be same as vertices.'
    np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), wt)
  return num_points 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def spikify_data(data_e, rng, dt=1.0, max_firing_rate=100):
  """ Apply spikes to a continuous dataset whose values are between 0.0 and 1.0
  Args:
    data_e: nexamples length list of NxT trials
    dt: how often the data are sampled
    max_firing_rate: the firing rate that is associated with a value of 1.0
  Returns:
    spikified_data_e: a list of length b of the data represented as spikes,
    sampled from the underlying poisson process.
    """

  spikifies_data_e = []
  E = len(data_e)
  spikes_e = []
  for e in range(E):
    data = data_e[e]
    N,T = data.shape
    data_s = np.zeros([N,T]).astype(np.int)
    for n in range(N):
      f = data[n,:]
      s = rng.poisson(f*max_firing_rate*dt, size=T)
      data_s[n,:] = s
    spikes_e.append(data_s)

  return spikes_e 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def GenerateSingleCode(code_shape):
  code = np.zeros(code_shape, dtype=np.int)

  keep_value_proba = 0.8

  height = code_shape[0]
  width = code_shape[1]
  depth = code_shape[2]

  for d in xrange(depth):
    for y in xrange(height):
      for x in xrange(width):
        v1 = ComputeLineCrc(code, width, y, x, d)
        v2 = ComputeDepthCrc(code, y, x, d)
        v = 1 if (v1 + v2 >= 6) else 0
        if np.random.rand() < keep_value_proba:
          code[y, x, d] = v
        else:
          code[y, x, d] = 1 - v

  return code 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def _evaluate_final(self, model, xy_test, batch_size, history):
        res = {}
        pred_test = None

        if 'val_acc' in history.history:
            res['val_acc'] = max(history.history['val_acc'])
            rev_ix = -1 - list(reversed(history.history['val_acc'])).index(res['val_acc'])
            res['val_loss'] = history.history['val_loss'][rev_ix]

        res['acc'] = history.history['acc'][-1]
        res['loss'] = history.history['loss'][-1]

        if len(xy_test[0]):
            from sklearn.metrics import classification_report, roc_auc_score
            # evaluate with test data
            x_test, y_test = xy_test
            pred_test = model.predict(x_test, batch_size=batch_size, verbose=0)
            test_loss, test_acc = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=0)
            res['test_loss'] = test_loss
            res['test_acc'] = test_acc

            report = classification_report(y_true = np.argmax(y_test, axis=1),
                                           y_pred = np.argmax(pred_test, axis=1),
                                           target_names=self.labels,
                                           digits=4,
                                           output_dict=True)

            res['auc'] = roc_auc_score(y_test.astype(np.int), pred_test)

            for label in self.labels:
                stats = report[label]
                res[label+"-precision"] = stats['precision']
                res[label+"-recall"] = stats['recall']
                res[label+"-f1"] = stats['f1-score']

        return pred_test, res 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int [as 別名]
def _load_yaml_and_configure(self, path, label, cache, device, device_memory): # pylint: disable=R0913
        doc = load_yaml_file(path, label)
        try:
            doc['device'] = parse_device(doc.get('device', {}),
                                         device_id=device,
                                         device_memory=device_memory)

            doc['data'] = parse_data(doc.get('data', {}), cache=cache, plugins=self.plugins)

            if 'random-seed' in doc and not isinstance(doc['random-seed'], int):
                raise VergeMLError('Invalid value option random-seed.',
                                   'random-seed must be an integer value.',
                                   hint_type='value',
                                   hint_key='random-seed')
        except VergeMLError as err:

            if err.hint_key:

                with open(path) as file:
                    definition = yaml_find_definition(file, err.hint_key, err.hint_type)

                if definition:
                    line, column, length = definition
                    err.message = display_err_in_file(path, line, column, str(err), length)
                    # clear suggestion because it is already contained in the error message.
                    err.suggestion = None
                    raise err
                else:
                    raise err
            else:
                raise err
        return doc