public ScatterFillExpression(TExpression src, SVar value, int dimension, TExpression indices) { this.src = src; this.value = value; this.dimension = dimension; this.indices = indices; }
public static SVar Lerp(SVar a, SVar b, SVar weight) { return(new SVar(new DelegateScalarExpression(() => LerpFloat(a.Evaluate(), b.Evaluate(), weight.Evaluate())))); }
// public TVar Pow(TVar y) { return new TVar(new BinaryScalarTensorExpression(this.Expression, y.Expression, Ops.Tpow)); } public static SVar Atan2(SVar y, SVar x) { return(new SVar(new DelegateScalarExpression(() => (float)Math.Atan2(y.Evaluate(), x.Evaluate())))); }
public SVar Clamp(SVar min, SVar max) { return(new SVar(new DelegateScalarExpression(() => ClampFloat(expression.Evaluate(), min.expression.Evaluate(), max.expression.Evaluate())))); }
public SVar Pow(SVar y) { return(new SVar(new BinaryScalarExpression(expression, y.expression, (xVal, yVal) => (float)Math.Pow(xVal, yVal)))); }
public static TVar Fill(SVar value, IAllocator allocator, DType type, params long[] sizes) { return(new TVar(new FillExpression(allocator, type, sizes, res => Ops.Fill(res, value.Evaluate())))); }
public static TVar RandomBernoulli(SeedSource seedSource, SVar p, IAllocator allocator, DType type, params long[] sizes) { return(new TVar(new FillExpression(allocator, type, sizes, res => Ops.RandomBernoulli(res, seedSource, p.Evaluate())))); }
public static TVar RandomCauchy(SeedSource seedSource, SVar median, SVar sigma, IAllocator allocator, DType type, params long[] sizes) { return(new TVar(new FillExpression(allocator, type, sizes, res => Ops.RandomCauchy(res, seedSource, median.Evaluate(), sigma.Evaluate())))); }
public static TVar RandomLogNormal(SeedSource seedSource, SVar mean, SVar stdv, IAllocator allocator, DType type, params long[] sizes) { return(new TVar(new FillExpression(allocator, type, sizes, res => Ops.RandomLogNormal(res, seedSource, mean.Evaluate(), stdv.Evaluate())))); }
public static TVar RandomUniform(SeedSource seedSource, SVar min, SVar max, IAllocator allocator, DType type, params long[] sizes) { return(new TVar(new FillExpression(allocator, type, sizes, res => Ops.RandomUniform(res, seedSource, min.Evaluate(), max.Evaluate())))); }
public static TVar RandomExponential(SeedSource seedSource, SVar lambda, IAllocator allocator, DType type, params long[] sizes) { return(new TVar(new FillExpression(allocator, type, sizes, res => Ops.RandomExponential(res, seedSource, lambda.Evaluate())))); }
// Returns a copy of this tensor, with the given indices filled with the given value. // If, when this op is evaluated, the write target is the same tensor as this, then the copy is unnecessary and is skipped. public TVar ScatterFill(SVar value, int dimension, TVar indices) { return(new TVar(new ScatterFillExpression(this.Expression, value, dimension, indices.Expression))); }
public static TVar Lerp(TVar a, TVar b, SVar weight) { return(new TVar(new BinaryTensorTensorExpression(a.Expression, b.Expression, (res, aVal, bVal) => Ops.Lerp(res, aVal, bVal, weight.Evaluate())))); }
public TVar Clamp(SVar min, SVar max) { return(new TVar(new UnaryTensorExpression(this.Expression, (res, src) => Ops.Clamp(res, src, min.Evaluate(), max.Evaluate())))); }
public TVar Pow(SVar y) { return(new TVar(new BinaryTensorScalarExpression(this.Expression, y.Expression, Ops.Pow))); }
public TVar Div(SVar rhs) { return(new TVar(new BinaryTensorScalarExpression(this.Expression, rhs.Expression, Ops.Div))); }