Python numpy.min方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def mutation(self, mutation_rate=0.01):
        active_check = False

        for n in range(self.net_info.node_num + self.net_info.out_num):
            t = self.gene[n][0]
            # mutation for type gene
            type_num = self.net_info.func_type_num if n < self.net_info.node_num else self.net_info.out_type_num
            if np.random.rand() < mutation_rate and type_num > 1:
                self.gene[n][0] = self.__mutate(self.gene[n][0], 0, type_num)
                if self.is_active[n]:
                    active_check = True
            # mutation for connection gene
            col = np.min((int(n / self.net_info.rows), self.net_info.cols))
            max_connect_id = col * self.net_info.rows + self.net_info.input_num
            min_connect_id = (col - self.net_info.level_back) * self.net_info.rows + self.net_info.input_num \
                if col - self.net_info.level_back >= 0 else 0
            in_num = self.net_info.func_in_num[t] if n < self.net_info.node_num else self.net_info.out_in_num[t]
            for i in range(self.net_info.max_in_num):
                if np.random.rand() < mutation_rate and max_connect_id - min_connect_id > 1:
                    self.gene[n][i+1] = self.__mutate(self.gene[n][i+1], min_connect_id, max_connect_id)
                    if self.is_active[n] and i < in_num:
                        active_check = True

        self.check_active()
        return active_check 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def neutral_mutation(self, mutation_rate=0.01):
        for n in range(self.net_info.node_num + self.net_info.out_num):
            t = self.gene[n][0]
            # mutation for type gene
            type_num = self.net_info.func_type_num if n < self.net_info.node_num else self.net_info.out_type_num
            if not self.is_active[n] and np.random.rand() < mutation_rate and type_num > 1:
                self.gene[n][0] = self.__mutate(self.gene[n][0], 0, type_num)
            # mutation for connection gene
            col = np.min((int(n / self.net_info.rows), self.net_info.cols))
            max_connect_id = col * self.net_info.rows + self.net_info.input_num
            min_connect_id = (col - self.net_info.level_back) * self.net_info.rows + self.net_info.input_num \
                if col - self.net_info.level_back >= 0 else 0
            in_num = self.net_info.func_in_num[t] if n < self.net_info.node_num else self.net_info.out_in_num[t]
            for i in range(self.net_info.max_in_num):
                if (not self.is_active[n] or i >= in_num) and np.random.rand() < mutation_rate \
                        and max_connect_id - min_connect_id > 1:
                    self.gene[n][i+1] = self.__mutate(self.gene[n][i+1], min_connect_id, max_connect_id)

        self.check_active()
        return False 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def load_RSM(filename):
    om, tt, psd = xu.io.getxrdml_map(filename)
    om = np.deg2rad(om)
    tt = np.deg2rad(tt)
    wavelength = 1.54056

    q_y = (1 / wavelength) * (np.cos(tt) - np.cos(2 * om - tt))
    q_x = (1 / wavelength) * (np.sin(tt) - np.sin(2 * om - tt))

    xi = np.linspace(np.min(q_x), np.max(q_x), 100)
    yi = np.linspace(np.min(q_y), np.max(q_y), 100)
    psd[psd < 1] = 1
    data_grid = griddata(
        (q_x, q_y), psd, (xi[None, :], yi[:, None]), fill_value=1, method="cubic"
    )
    nx, ny = data_grid.shape

    range_values = [np.min(q_x), np.max(q_x), np.min(q_y), np.max(q_y)]
    output_data = (
        Panel(np.log(data_grid).reshape(nx, ny, 1), minor_axis=["RSM"])
        .transpose(2, 0, 1)
        .to_frame()
    )

    return range_values, output_data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def create_mnist(tfrecord_dir, mnist_dir):
    print('Loading MNIST from "%s"' % mnist_dir)
    import gzip
    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
        images = np.frombuffer(file.read(), np.uint8, offset=16)
    with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file:
        labels = np.frombuffer(file.read(), np.uint8, offset=8)
    images = images.reshape(-1, 1, 28, 28)
    images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0)
    assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8
    assert labels.shape == (60000,) and labels.dtype == np.uint8
    assert np.min(images) == 0 and np.max(images) == 255
    assert np.min(labels) == 0 and np.max(labels) == 9
    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
    onehot[np.arange(labels.size), labels] = 1.0
    
    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
        order = tfr.choose_shuffled_order()
        for idx in range(order.size):
            tfr.add_image(images[order[idx]])
        tfr.add_labels(onehot[order])

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

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123):
    print('Loading MNIST from "%s"' % mnist_dir)
    import gzip
    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
        images = np.frombuffer(file.read(), np.uint8, offset=16)
    images = images.reshape(-1, 28, 28)
    images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
    assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
    assert np.min(images) == 0 and np.max(images) == 255
    
    with TFRecordExporter(tfrecord_dir, num_images) as tfr:
        rnd = np.random.RandomState(random_seed)
        for idx in range(num_images):
            tfr.add_image(images[rnd.randint(images.shape[0], size=3)])

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

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def create_cifar100(tfrecord_dir, cifar100_dir):
    print('Loading CIFAR-100 from "%s"' % cifar100_dir)
    import pickle
    with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
        data = pickle.load(file, encoding='latin1')
    images = data['data'].reshape(-1, 3, 32, 32)
    labels = np.array(data['fine_labels'])
    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
    assert labels.shape == (50000,) and labels.dtype == np.int32
    assert np.min(images) == 0 and np.max(images) == 255
    assert np.min(labels) == 0 and np.max(labels) == 99
    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
    onehot[np.arange(labels.size), labels] = 1.0

    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
        order = tfr.choose_shuffled_order()
        for idx in range(order.size):
            tfr.add_image(images[order[idx]])
        tfr.add_labels(onehot[order])

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

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def test_equ_random_sample_scalar(self):
        """
        Test that random sample of reddening at arbitary distance is actually
        from the set of possible reddening samples at that distance. Uses vector
        of coordinates/distances as input. Uses single set of
        coordinates/distance as input.
        """
        for d in self._test_data:
            # Prepare coordinates (with random distances)
            l = d['l']*units.deg
            b = d['b']*units.deg
            dm = 3. + (25.-3.)*np.random.random()

            dist = 10.**(dm/5.-2.)
            c = coords.SkyCoord(l, b, distance=dist*units.kpc, frame='galactic')

            ebv_data = self._interp_ebv(d, dist)
            ebv_calc = self._bayestar(c, mode='random_sample')

            d_ebv = np.min(np.abs(ebv_data[:] - ebv_calc))

            np.testing.assert_allclose(d_ebv, 0., atol=0.001, rtol=0.0001) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def test_equ_random_sample_nodist_vector(self):
        """
        Test that a random sample of the reddening vs. distance curve is drawn
        from the full set of samples. Uses vector of coordinates as input.
        """

        # Prepare coordinates
        l = [d['l']*units.deg for d in self._test_data]
        b = [d['b']*units.deg for d in self._test_data]

        c = coords.SkyCoord(l, b, frame='galactic')

        ebv_data = np.array([d['samples'] for d in self._test_data])
        ebv_calc = self._bayestar(c, mode='random_sample')

        # print 'vector random sample:'
        # print 'ebv_data.shape = {}'.format(ebv_data.shape)
        # print 'ebv_calc.shape = {}'.format(ebv_calc.shape)
        # print ebv_data[0]
        # print ebv_calc[0]

        d_ebv = np.min(np.abs(ebv_data[:,:,:] - ebv_calc[:,None,:]), axis=1)
        np.testing.assert_allclose(d_ebv, 0., atol=0.001, rtol=0.0001) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def wave2input_image(wave, window, pos=0, pad=0):
    wave_image = np.hstack([wave[pos+i*sride:pos+(i+pad*2)*sride+dif].reshape(height+pad*2, sride) for i in range(256//sride)])[:,:254]
    wave_image *= window
    spectrum_image = np.fft.fft(wave_image, axis=1)
    input_image = np.abs(spectrum_image[:,:128].reshape(1, height+pad*2, 128), dtype=np.float32)

    np.clip(input_image, 1000, None, out=input_image)
    np.log(input_image, out=input_image)
    input_image += bias
    input_image /= scale

    if np.max(input_image) > 0.95:
        print('input image max bigger than 0.95', np.max(input_image))
    if np.min(input_image) < 0.05:
        print('input image min smaller than 0.05', np.min(input_image))

    return input_image 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def cmap(self, data=None):
        '''
        lblidx.cmap() yields a colormap for the given label index object that assumes that the data
          being plotted will be rescaled such that label 0 is 0 and the highest label value in the
          label index is equal to 1.
        lblidx.cmap(data) yields a colormap that will correctly color the labels given in data if
          data is scaled such that its minimum and maximum value are 0 and 1.
        '''
        import matplotlib.colors
        from_list = matplotlib.colors.LinearSegmentedColormap.from_list
        if data is None: return self.colormap
        data = np.asarray(data).flatten()
        (vmin,vmax) = (np.min(data), np.max(data))
        ii  = np.argsort(self.ids)
        ids = np.asarray(self.ids)[ii]
        if vmin == vmax:
            (vmin,vmax,ii) = (vmin-0.5, vmax+0.5, vmin)
            clr = self.color_lookup(ii)
            return from_list('label1', [(0, clr), (1, clr)])
        q   = (ids >= vmin) & (ids <= vmax)
        ids = ids[q]
        clrs = self.color_lookup(ids)
        vals = (ids - vmin) / (vmax - vmin)
        return from_list('label%d' % len(vals), list(zip(vals, clrs))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def resize(im, short, max_size):
    """
    only resize input image to target size and return scale
    :param im: BGR image input by opencv
    :param short: one dimensional size (the short side)
    :param max_size: one dimensional max size (the long side)
    :return: resized image (NDArray) and scale (float)
    """
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(short) / float(im_size_min)
    # prevent bigger axis from being more than max_size:
    if np.round(im_scale * im_size_max) > max_size:
        im_scale = float(max_size) / float(im_size_max)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
    return im, im_scale 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def collect(self, name, arr):
        """Callback function for collecting min and max values from an NDArray."""
        name = py_str(name)
        if self.include_layer is not None and not self.include_layer(name):
            return
        handle = ctypes.cast(arr, NDArrayHandle)
        arr = NDArray(handle, writable=False)
        min_range = ndarray.min(arr).asscalar()
        max_range = ndarray.max(arr).asscalar()
        if name in self.min_max_dict:
            cur_min_max = self.min_max_dict[name]
            self.min_max_dict[name] = (min(cur_min_max[0], min_range),
                                       max(cur_min_max[1], max_range))
        else:
            self.min_max_dict[name] = (min_range, max_range)
        if self.logger is not None:
            self.logger.info("Collecting layer %s output min_range=%f, max_range=%f"
                             % (name, min_range, max_range)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def test_quantize_float32_to_int8():
    shape = rand_shape_nd(4)
    data = rand_ndarray(shape, 'default', dtype='float32')
    min_range = mx.nd.min(data)
    max_range = mx.nd.max(data)
    qdata, min_val, max_val = mx.nd.contrib.quantize(data, min_range, max_range, out_type='int8')
    data_np = data.asnumpy()
    min_range = min_range.asscalar()
    max_range = max_range.asscalar()
    real_range = np.maximum(np.abs(min_range), np.abs(max_range))
    quantized_range = 127.0
    scale = quantized_range / real_range
    assert qdata.dtype == np.int8
    assert min_val.dtype == np.float32
    assert max_val.dtype == np.float32
    assert same(min_val.asscalar(), -real_range)
    assert same(max_val.asscalar(), real_range)
    qdata_np = (np.sign(data_np) * np.minimum(np.abs(data_np) * scale + 0.5, quantized_range)).astype(np.int8)
    assert_almost_equal(qdata.asnumpy(), qdata_np, atol = 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def get_optimal_action(self, current_node_ids, step_number):
    """Returns the optimal action from the current node."""
    goal_number = step_number / self.task_params.num_steps
    gtG = self.task.gtG
    a = np.zeros((len(current_node_ids), self.task_params.num_actions), dtype=np.int32)
    d_dict = self.episode.dist_to_goal[goal_number]
    for i, c in enumerate(current_node_ids):
      neigh = gtG.vertex(c).out_neighbours()
      neigh_edge = gtG.vertex(c).out_edges()
      ds = np.array([d_dict[i][int(x)] for x in neigh])
      ds_min = np.min(ds)
      for i_, e in enumerate(neigh_edge):
        if ds[i_] == ds_min:
          _ = gtG.ep['action'][e]
          a[i, _] = 1
    return a 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import min [as 別名]
def _line_to_xy(self, line_x, line_y, limit_y_min=None, limit_y_max=None):
        point_every = max(1, int(len(line_x) / self.nb_points_max))
        points_x = []
        points_y = []
        curr_sum = 0
        counter = 0
        last_idx = len(line_x) - 1
        for i in range(len(line_x)):
            batch_idx = line_x[i]
            if batch_idx > self.start_batch_idx:
                curr_sum += line_y[i]
                counter += 1
                if counter >= point_every or i == last_idx:
                    points_x.append(batch_idx)
                    y = curr_sum / counter
                    if limit_y_min is not None and limit_y_max is not None:
                        y = np.clip(y, limit_y_min, limit_y_max)
                    elif limit_y_min is not None:
                        y = max(y, limit_y_min)
                    elif limit_y_max is not None:
                        y = min(y, limit_y_max)
                    points_y.append(y)
                    counter = 0
                    curr_sum = 0

        return points_x, points_y