/** * Returns the overall variance of this ndarray * * @param biasCorrected boolean on whether to apply corrected bias * @param dimension the dimension to getScalar the mean along * @return the mean along the specified dimension of this ndarray */ @Override public INDArray var(boolean biasCorrected, int... dimension) { return Nd4j.getExecutioner().exec(new Variance(this, biasCorrected), dimension); }
public SDVariable variance(SDVariable i_x, boolean biasCorrected, int... dimensions) { return new Variance(sameDiff(), i_x, dimensions, biasCorrected).outputVariables()[0]; }
/** * Returns the overall variance of this ndarray * * @param dimension the dimension to getScalar the mean along * @return the mean along the specified dimension of this ndarray */ @Override public INDArray var(int... dimension) { return Nd4j.getExecutioner().exec(new Variance(this), dimension); }
break; case "var": ret = new Variance(x, y, z, x.length(),(boolean) extraArgs[0]); break; default:
/** * Returns the overall variance of this ndarray * * @param dimension the dimension to getScalar the mean along * @return the mean along the specified dimension of this ndarray */ @Override public INDArray var(int... dimension) { return Nd4j.getExecutioner().exec(new Variance(this), dimension); }
/** * Returns the overall variance of this ndarray * * @param biasCorrected boolean on whether to apply corrected bias * @param dimension the dimension to getScalar the mean along * @return the mean along the specified dimension of this ndarray */ @Override public INDArray var(boolean biasCorrected, int... dimension) { return Nd4j.getExecutioner().exec(new Variance(this, biasCorrected), dimension); }
@Override public Variance opForDimension(int index, int... dimension) { INDArray xAlongDimension = x.tensorAlongDimension(index, dimension); Variance ret; if (y() != null) ret = new Variance(xAlongDimension, y.tensorAlongDimension(index, dimension), xAlongDimension.length()); else ret = new Variance(x.tensorAlongDimension(index, dimension), biasCorrected); ret.setApplyFinalTransform(applyFinalTransform()); return ret; }
@Override public Op opForDimension(int index, int dimension) { INDArray xAlongDimension = x.vectorAlongDimension(index, dimension); Variance ret; if (y() != null) ret = new Variance(xAlongDimension, y.vectorAlongDimension(index, dimension), xAlongDimension.length()); else ret = new Variance(x.vectorAlongDimension(index, dimension)); ret.setBiasCorrected(biasCorrected); ret.setApplyFinalTransform(applyFinalTransform()); return ret; }
break; case "var": ret = new Variance(x, y,z, x.length(),(boolean) extraArgs[0]); break; case "euclidean":