mindspore.numpy.histogram2d
- mindspore.numpy.histogram2d(x, y, bins=10, range=None, weights=None, density=False)[source]
Computes the multidimensional histogram of some data.
Note
Deprecated numpy argument normed is not supported.
- Parameters
x (Union[list, tuple, Tensor]) – An array with shape (N,) containing the x coordinates of the points to be histogrammed.
y (Union[list, tuple, Tensor]) – An array with shape (N,) containing the y coordinates of the points to be histogrammed.
bins (Union[int, tuple, list], optional) –
The bin specification:
If int, the number of bins for the two dimensions
(nx=ny=bins)
.If array_like, the bin edges for the two dimensions
(x_edges=y_edges=bins)
.If [int, int], the number of bins in each dimension
(nx, ny = bins)
.If [array, array], the bin edges in each dimension
(x_edges, y_edges = bins)
.A combination [int, array] or [array, int], where int is the number of bins and array is the bin edges.
range (Union[list, tuple], optional) – has shape (2, 2), the leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the bins parameters):
[[xmin, xmax], [ymin, ymax]]
. All values outside of this range will be considered outliers and not tallied in the histogram.weights (Union[list, tuple, Tensor], optional) – An array with shape (N,) of values w_i weighing each sample (x_i, y_i).
density (boolean, optional) – If False, the default, returns the number of samples in each bin. If True, returns the probability density function at the bin,
bin_count / sample_count / bin_volume
.
- Returns
(Tensor, Tensor, Tensor), the values of the bi-directional histogram and the bin edges along the first and second dimensions.
- Raises
ValueError – if range does not have the same size as the number of samples.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> from mindspore import numpy as np >>> x = np.arange(5) >>> y = np.arange(2, 7) >>> print(np.histogram2d(x, y, bins=(2, 3))) (Tensor(shape=[2, 3], dtype=Float32, value= [[ 2.00000000e+00, 0.00000000e+00, 0.00000000e+00], [ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00]]), Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00, 2.00000000e+00, 4.00000000e+00]), Tensor(shape=[4], dtype=Float32, value= [ 2.00000000e+00, 3.33333349e+00, 4.66666698e+00, 6.00000000e+00]))