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Numpy.Math.cs
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56 lines (42 loc) · 1.82 KB
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using System;
using System.Collections;
using System.Collections.Generic;
using System.Numerics;
using System.Text;
using static Tensorflow.Binding;
namespace Tensorflow.NumPy
{
public partial class np
{
[AutoNumPy]
public static NDArray cos(NDArray x) => new NDArray(math_ops.cos(x));
[AutoNumPy]
public static NDArray exp(NDArray x) => new NDArray(tf.exp(x));
[AutoNumPy]
public static NDArray floor(NDArray x) => new NDArray(math_ops.floor(x));
[AutoNumPy]
public static NDArray log(NDArray x) => new NDArray(tf.log(x));
[AutoNumPy]
public static NDArray mean(NDArray x) => new NDArray(math_ops.reduce_mean(x));
[AutoNumPy]
public static NDArray multiply(NDArray x1, NDArray x2) => new NDArray(tf.multiply(x1, x2));
[AutoNumPy]
public static NDArray maximum(NDArray x1, NDArray x2) => new NDArray(tf.maximum(x1, x2));
[AutoNumPy]
public static NDArray minimum(NDArray x1, NDArray x2) => new NDArray(tf.minimum(x1, x2));
[AutoNumPy]
public static NDArray prod(NDArray array, Axis? axis = null, Type? dtype = null, bool keepdims = false)
=> new NDArray(tf.reduce_prod(array, axis: axis));
[AutoNumPy]
public static NDArray prod<T>(params T[] array) where T : unmanaged
=> new NDArray(tf.reduce_prod(new NDArray(array)));
[AutoNumPy]
public static NDArray power(NDArray x, NDArray y) => new NDArray(tf.pow(x, y));
[AutoNumPy]
public static NDArray sin(NDArray x) => new NDArray(math_ops.sin(x));
[AutoNumPy]
public static NDArray sqrt(NDArray x) => new NDArray(tf.sqrt(x));
[AutoNumPy]
public static NDArray sum(NDArray x1, Axis? axis = null) => new NDArray(tf.math.sum(x1, axis));
}
}