Backends/SiaNet.Backend.MxNet/NDArrayOps.cs
/// <summary>
/// Draw random samples from a uniform distribution... note:: The existing alias ``uniform`` is deprecated.Samples are uniformly distributed over the half-open interval *[low, high)*(includes *low*, but excludes *high*).Example::   uniform(low=0, high=1, shape=(2,2)) = [[ 0.60276335,  0.85794562],                                          [ 0.54488319,  0.84725171]]Defined in G:\deeplearn\mxnet\src\operator\random\sample_op.cc:L64
/// </summary>
/// <param name="@out">output NDArray</param>
/// <param name="low">Lower bound of the distribution.</param>
/// <param name="high">Upper bound 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 uniform distribution...
Draw random samples from a uniform distribution ...
public static NDArray RandomUniform(NDArray @out,
        float low = 0f,
        float high = 1f,
        Shape shape = null,
        Context ctx = null,
        DType dtype = null)
        {
            return new Operator("_random_uniform")
            .SetParam("low", low)
            .SetParam("high", high)
            .SetParam("shape", shape)
            .SetParam("ctx", ctx)
            .SetParam("dtype", dtype)
            .Invoke(@out);
        }
public static NDArray RandomUniform ( NDArray @out , float low = 0f , float high = 1f , Shape shape = null , Context ctx = null , DType dtype = null ) { return new Operator ( " " ) . SetParam ( " " , low ) . SetParam ( " " , high ) . SetParam ( " " , shape ) . SetParam ( " " , ctx ) . SetParam ( " " , dtype ) . Invoke ( @out ) ; }
Draw random samples from a uniform distribution... note:: The existing alias ``uniform`` is deprecated.Samples are uniformly distributed over the half-open interval *[low, high)*(includes *low*, but excludes *high*).Example::   uniform(low=0, high=1, shape=(2,2)) = [[ 0.60276335,  0.85794562],                                          [ 0.54488319,  0.84725171]]Defined in G:\deeplearn\mxnet\src\operator\random\sample_op.cc:L64
Draw random samples from a uniform distribution ... note :: The existing alias ` ` uniform ` ` is deprecated . Samples are uniformly distributed over the half - open interval * [ low high ) * ( includes * low * but excludes * high * ) . Example :: uniform ( low = 0 high = 1 shape = ( 2 2 )) = [[ 0 . 60276335 0 . 85794562 ] [ 0 . 54488319 0 . 84725171 ]] Defined in G : \ deeplearn \ mxnet \ src \ operator \ random \ sample_op . cc : L64
returns new symbol
returns new symbol
@out
NDArray
None
low
float
Lower bound of the distribution.
Lower bound of the distribution .
high
float
Upper bound of the distribution.
Upper bound 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 ) .