Backends/SiaNet.Backend.MxNet/NDArrayOps.cs
/// <summary>
/// Draw random samples from a normal (Gaussian) distribution... note:: The existing alias ``normal`` is deprecated.Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation).Example::   normal(loc=0, scale=1, shape=(2,2)) = [[ 1.89171135, -1.16881478],                                          [-1.23474145,  1.55807114]]Defined in G:\deeplearn\mxnet\src\operator\random\sample_op.cc:L83
/// </summary>
/// <param name="@out">output NDArray</param>
/// <param name="loc">Mean of the distribution.</param>
/// <param name="scale">Standard deviation of the distribution.</param>
/// <param name="shape">Shape of the output.</param>
/// <param name="ctx">Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</param>
/// <param name="dtype">DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</param>
/// <returns>returns new symbol</returns>
Draw random samples from a normal (Gaussian) distribution...
Draw random samples from a normal ( Gaussian ) distribution ...
public static NDArray RandomNormal(NDArray @out,
        float loc = 0f,
        float scale = 1f,
        Shape shape = null,
        Context ctx = null,
        DType dtype = null)
        {
            return new Operator("_random_normal")
            .SetParam("loc", loc)
            .SetParam("scale", scale)
            .SetParam("shape", shape)
            .SetParam("ctx", ctx)
            .SetParam("dtype", dtype)
            .Invoke(@out);
        }
public static NDArray RandomNormal ( NDArray @out , float loc = 0f , float scale = 1f , Shape shape = null , Context ctx = null , DType dtype = null ) { return new Operator ( " " ) . SetParam ( " " , loc ) . SetParam ( " " , scale ) . SetParam ( " " , shape ) . SetParam ( " " , ctx ) . SetParam ( " " , dtype ) . Invoke ( @out ) ; }
Draw random samples from a normal (Gaussian) distribution... note:: The existing alias ``normal`` is deprecated.Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation).Example::   normal(loc=0, scale=1, shape=(2,2)) = [[ 1.89171135, -1.16881478],                                          [-1.23474145,  1.55807114]]Defined in G:\deeplearn\mxnet\src\operator\random\sample_op.cc:L83
Draw random samples from a normal ( Gaussian ) distribution ... note :: The existing alias ` ` normal ` ` is deprecated . Samples are distributed according to a normal distribution parametrized by * loc * ( mean ) and * scale * ( standard deviation ) . Example :: normal ( loc = 0 scale = 1 shape = ( 2 2 )) = [[ 1 . 89171135 - 1 . 16881478 ] [ - 1 . 23474145 1 . 55807114 ]] Defined in G : \ deeplearn \ mxnet \ src \ operator \ random \ sample_op . cc : L83
returns new symbol
returns new symbol
@out
NDArray
None
loc
float
Mean of the distribution.
Mean of the distribution .
scale
float
Standard deviation of the distribution.
Standard deviation of the distribution .
shape
Shape
Shape of the output.
Shape of the output .
ctx
Context
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
Context of output in format [ cpu|gpu|cpu_pinned ] ( n ) . Only used for imperative calls .
dtype
DType
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
DType of the output in case this can ' t be inferred . Defaults to float32 if not defined ( dtype = None ) .