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3275 lines (3224 loc) · 158 KB
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// Copyright (c) 2019 by the SciSharp Team
// Code generated by CodeMinion: https://github.com/SciSharp/CodeMinion
using System;
using System.Collections;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text;
using Python.Runtime;
using Numpy.Models;
namespace Numpy
{
public partial class NumPy
{
/// <summary>
/// Return the minimum of an array or minimum along an axis.
///
/// Notes
///
/// NaN values are propagated, that is if at least one item is NaN, the
/// corresponding min value will be NaN as well. To ignore NaN values
/// (MATLAB behavior), please use nanmin.
///
/// Don’t use amin for element-wise comparison of 2 arrays; when
/// a.shape[0] is 2, minimum(a[0], a[1]) is faster than
/// amin(a, axis=0).
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Axis or axes along which to operate. By default, flattened input is
/// used.
///
/// If this is a tuple of ints, the minimum is selected over multiple axes,
/// instead of a single axis or all the axes as before.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. Must
/// be of the same shape and buffer length as the expected output.
/// See doc.ufuncs (Section “Output arguments”) for more details.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the input array.
///
/// If the default value is passed, then keepdims will not be
/// passed through to the amin method of sub-classes of
/// ndarray, however any non-default value will be. If the
/// sub-class’ method does not implement keepdims any
/// exceptions will be raised.
/// </param>
/// <param name="initial">
/// The maximum value of an output element. Must be present to allow
/// computation on empty slice. See reduce for details.
/// </param>
/// <returns>
/// Minimum of a. If axis is None, the result is a scalar value.
/// If axis is given, the result is an array of dimension
/// a.ndim - 1.
/// </returns>
public NDarray amin(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null, ValueType initial = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (keepdims!=null) kwargs["keepdims"]=ToPython(keepdims);
if (initial!=null) kwargs["initial"]=ToPython(initial);
dynamic py = __self__.InvokeMethod("amin", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return the maximum of an array or maximum along an axis.
///
/// Notes
///
/// NaN values are propagated, that is if at least one item is NaN, the
/// corresponding max value will be NaN as well. To ignore NaN values
/// (MATLAB behavior), please use nanmax.
///
/// Don’t use amax for element-wise comparison of 2 arrays; when
/// a.shape[0] is 2, maximum(a[0], a[1]) is faster than
/// amax(a, axis=0).
/// </summary>
/// <param name="a">
/// Input data.
/// </param>
/// <param name="axis">
/// Axis or axes along which to operate. By default, flattened input is
/// used.
///
/// If this is a tuple of ints, the maximum is selected over multiple axes,
/// instead of a single axis or all the axes as before.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. Must
/// be of the same shape and buffer length as the expected output.
/// See doc.ufuncs (Section “Output arguments”) for more details.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the input array.
///
/// If the default value is passed, then keepdims will not be
/// passed through to the amax method of sub-classes of
/// ndarray, however any non-default value will be. If the
/// sub-class’ method does not implement keepdims any
/// exceptions will be raised.
/// </param>
/// <param name="initial">
/// The minimum value of an output element. Must be present to allow
/// computation on empty slice. See reduce for details.
/// </param>
/// <returns>
/// Maximum of a. If axis is None, the result is a scalar value.
/// If axis is given, the result is an array of dimension
/// a.ndim - 1.
/// </returns>
public NDarray amax(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null, ValueType initial = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (keepdims!=null) kwargs["keepdims"]=ToPython(keepdims);
if (initial!=null) kwargs["initial"]=ToPython(initial);
dynamic py = __self__.InvokeMethod("amax", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return minimum of an array or minimum along an axis, ignoring any NaNs.
/// When all-NaN slices are encountered a RuntimeWarning is raised and
/// Nan is returned for that slice.
///
/// Notes
///
/// NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
/// (IEEE 754). This means that Not a Number is not equivalent to infinity.
/// Positive infinity is treated as a very large number and negative
/// infinity is treated as a very small (i.e. negative) number.
///
/// If the input has a integer type the function is equivalent to np.min.
/// </summary>
/// <param name="a">
/// Array containing numbers whose minimum is desired. If a is not an
/// array, a conversion is attempted.
/// </param>
/// <param name="axis">
/// Axis or axes along which the minimum is computed. The default is to compute
/// the minimum of the flattened array.
/// </param>
/// <param name="@out">
/// Alternate output array in which to place the result. The default
/// is None; if provided, it must have the same shape as the
/// expected output, but the type will be cast if necessary. See
/// doc.ufuncs for details.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the original a.
///
/// If the value is anything but the default, then
/// keepdims will be passed through to the min method
/// of sub-classes of ndarray. If the sub-classes methods
/// does not implement keepdims any exceptions will be raised.
/// </param>
/// <returns>
/// An array with the same shape as a, with the specified axis
/// removed. If a is a 0-d array, or if axis is None, an ndarray
/// scalar is returned. The same dtype as a is returned.
/// </returns>
public NDarray nanmin(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (keepdims!=null) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("nanmin", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Return the maximum of an array or maximum along an axis, ignoring any
/// NaNs. When all-NaN slices are encountered a RuntimeWarning is
/// raised and NaN is returned for that slice.
///
/// Notes
///
/// NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
/// (IEEE 754). This means that Not a Number is not equivalent to infinity.
/// Positive infinity is treated as a very large number and negative
/// infinity is treated as a very small (i.e. negative) number.
///
/// If the input has a integer type the function is equivalent to np.max.
/// </summary>
/// <param name="a">
/// Array containing numbers whose maximum is desired. If a is not an
/// array, a conversion is attempted.
/// </param>
/// <param name="axis">
/// Axis or axes along which the maximum is computed. The default is to compute
/// the maximum of the flattened array.
/// </param>
/// <param name="@out">
/// Alternate output array in which to place the result. The default
/// is None; if provided, it must have the same shape as the
/// expected output, but the type will be cast if necessary. See
/// doc.ufuncs for details.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the original a.
///
/// If the value is anything but the default, then
/// keepdims will be passed through to the max method
/// of sub-classes of ndarray. If the sub-classes methods
/// does not implement keepdims any exceptions will be raised.
/// </param>
/// <returns>
/// An array with the same shape as a, with the specified axis removed.
/// If a is a 0-d array, or if axis is None, an ndarray scalar is
/// returned. The same dtype as a is returned.
/// </returns>
public NDarray nanmax(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (keepdims!=null) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("nanmax", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Range of values (maximum - minimum) along an axis.
///
/// The name of the function comes from the acronym for ‘peak to peak’.
/// </summary>
/// <param name="a">
/// Input values.
/// </param>
/// <param name="axis">
/// Axis along which to find the peaks. By default, flatten the
/// array. axis may be negative, in
/// which case it counts from the last to the first axis.
///
/// If this is a tuple of ints, a reduction is performed on multiple
/// axes, instead of a single axis or all the axes as before.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type of the output values will be cast if necessary.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the input array.
///
/// If the default value is passed, then keepdims will not be
/// passed through to the ptp method of sub-classes of
/// ndarray, however any non-default value will be. If the
/// sub-class’ method does not implement keepdims any
/// exceptions will be raised.
/// </param>
/// <returns>
/// A new array holding the result, unless out was
/// specified, in which case a reference to out is returned.
/// </returns>
public NDarray ptp(NDarray a, int[] axis = null, NDarray @out = null, bool? keepdims = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (keepdims!=null) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("ptp", pyargs, kwargs);
return ToCsharp<NDarray>(py);
}
/// <summary>
/// Compute the q-th percentile of the data along the specified axis.
///
/// Returns the q-th percentile(s) of the array elements.
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V. The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly. This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the percentiles are computed. The
/// default is to compute the percentile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left in
/// the result as dimensions with size one. With this option, the
/// result will broadcast correctly against the original array a.
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar. If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public NDarray<double> percentile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = false)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
if (keepdims!=false) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("percentile", pyargs, kwargs);
return ToCsharp<NDarray<double>>(py);
}
/// <summary>
/// Compute the q-th percentile of the data along the specified axis.
///
/// Returns the q-th percentile(s) of the array elements.
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V. The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly. This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar. If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public double percentile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
dynamic py = __self__.InvokeMethod("percentile", pyargs, kwargs);
return ToCsharp<double>(py);
}
/// <summary>
/// Compute the qth percentile of the data along the specified axis,
/// while ignoring nan values.
///
/// Returns the qth percentile(s) of the array elements.
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V. The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly. This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the percentiles are computed. The
/// default is to compute the percentile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left in
/// the result as dimensions with size one. With this option, the
/// result will broadcast correctly against the original array a.
///
/// If this is anything but the default value it will be passed
/// through (in the special case of an empty array) to the
/// mean function of the underlying array. If the array is
/// a sub-class and mean does not have the kwarg keepdims this
/// will raise a RuntimeError.
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar. If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public NDarray<double> nanpercentile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
if (keepdims!=null) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("nanpercentile", pyargs, kwargs);
return ToCsharp<NDarray<double>>(py);
}
/// <summary>
/// Compute the qth percentile of the data along the specified axis,
/// while ignoring nan values.
///
/// Returns the qth percentile(s) of the array elements.
///
/// Notes
///
/// Given a vector V of length N, the q-th percentile of
/// V is the value q/100 of the way from the minimum to the
/// maximum in a sorted copy of V. The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the percentile if the normalized ranking does not
/// match the location of q exactly. This function is the same as
/// the median if q=50, the same as the minimum if q=0 and the
/// same as the maximum if q=100.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored.
/// </param>
/// <param name="q">
/// Percentile or sequence of percentiles to compute, which must be between
/// 0 and 100 inclusive.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired percentile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar. If multiple percentiles are given, first axis of
/// the result corresponds to the percentiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public double nanpercentile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
dynamic py = __self__.InvokeMethod("nanpercentile", pyargs, kwargs);
return ToCsharp<double>(py);
}
/// <summary>
/// Compute the q-th quantile of the data along the specified axis.
/// ..versionadded:: 1.15.0
///
/// Notes
///
/// Given a vector V of length N, the q-th quantile of
/// V is the value q of the way from the minimum to the
/// maximum in a sorted copy of V. The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the quantile if the normalized ranking does not
/// match the location of q exactly. This function is the same as
/// the median if q=0.5, the same as the minimum if q=0.0 and the
/// same as the maximum if q=1.0.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the quantiles are computed. The
/// default is to compute the quantile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left in
/// the result as dimensions with size one. With this option, the
/// result will broadcast correctly against the original array a.
/// </param>
/// <returns>
/// If q is a single quantile and axis=None, then the result
/// is a scalar. If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public NDarray<double> quantile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = false)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
if (keepdims!=false) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("quantile", pyargs, kwargs);
return ToCsharp<NDarray<double>>(py);
}
/// <summary>
/// Compute the q-th quantile of the data along the specified axis.
/// ..versionadded:: 1.15.0
///
/// Notes
///
/// Given a vector V of length N, the q-th quantile of
/// V is the value q of the way from the minimum to the
/// maximum in a sorted copy of V. The values and distances of
/// the two nearest neighbors as well as the interpolation parameter
/// will determine the quantile if the normalized ranking does not
/// match the location of q exactly. This function is the same as
/// the median if q=0.5, the same as the minimum if q=0.0 and the
/// same as the maximum if q=1.0.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single quantile and axis=None, then the result
/// is a scalar. If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public double quantile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
dynamic py = __self__.InvokeMethod("quantile", pyargs, kwargs);
return ToCsharp<double>(py);
}
/// <summary>
/// Compute the qth quantile of the data along the specified axis,
/// while ignoring nan values.
/// Returns the qth quantile(s) of the array elements.
/// .. versionadded:: 1.15.0
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="axis">
/// Axis or axes along which the quantiles are computed. The
/// default is to compute the quantile(s) along a flattened
/// version of the array.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left in
/// the result as dimensions with size one. With this option, the
/// result will broadcast correctly against the original array a.
///
/// If this is anything but the default value it will be passed
/// through (in the special case of an empty array) to the
/// mean function of the underlying array. If the array is
/// a sub-class and mean does not have the kwarg keepdims this
/// will raise a RuntimeError.
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar. If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public NDarray<double> nanquantile(NDarray a, NDarray<float> q, int[] axis, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear", bool? keepdims = null)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
if (keepdims!=null) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("nanquantile", pyargs, kwargs);
return ToCsharp<NDarray<double>>(py);
}
/// <summary>
/// Compute the qth quantile of the data along the specified axis,
/// while ignoring nan values.
/// Returns the qth quantile(s) of the array elements.
/// .. versionadded:: 1.15.0
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array, containing
/// nan values to be ignored
/// </param>
/// <param name="q">
/// Quantile or sequence of quantiles to compute, which must be between
/// 0 and 1 inclusive.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow the input array a to be modified by intermediate
/// calculations, to save memory. In this case, the contents of the input
/// a after this function completes is undefined.
/// </param>
/// <param name="interpolation">
/// This optional parameter specifies the interpolation method to
/// use when the desired quantile lies between two data points
/// i < j:
/// </param>
/// <returns>
/// If q is a single percentile and axis=None, then the result
/// is a scalar. If multiple quantiles are given, first axis of
/// the result corresponds to the quantiles. The other axes are
/// the axes that remain after the reduction of a. If the input
/// contains integers or floats smaller than float64, the output
/// data-type is float64. Otherwise, the output data-type is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public double nanquantile(NDarray a, NDarray<float> q, NDarray @out = null, bool? overwrite_input = false, string interpolation = "linear")
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
q,
});
var kwargs=new PyDict();
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (interpolation!="linear") kwargs["interpolation"]=ToPython(interpolation);
dynamic py = __self__.InvokeMethod("nanquantile", pyargs, kwargs);
return ToCsharp<double>(py);
}
/// <summary>
/// Compute the median along the specified axis.
///
/// Returns the median of the array elements.
///
/// Notes
///
/// Given a vector V of length N, the median of V is the
/// middle value of a sorted copy of V, V_sorted - i
/// e., V_sorted[(N-1)/2], when N is odd, and the average of the
/// two middle values of V_sorted when N is even.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="axis">
/// Axis or axes along which the medians are computed. The default
/// is to compute the median along a flattened version of the array.
/// A sequence of axes is supported since version 1.9.0.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow use of memory of input array a for
/// calculations. The input array will be modified by the call to
/// median. This will save memory when you do not need to preserve
/// the contents of the input array. Treat the input as undefined,
/// but it will probably be fully or partially sorted. Default is
/// False. If overwrite_input is True and a is not already an
/// ndarray, an error will be raised.
/// </param>
/// <param name="keepdims">
/// If this is set to True, the axes which are reduced are left
/// in the result as dimensions with size one. With this option,
/// the result will broadcast correctly against the original arr.
/// </param>
/// <returns>
/// A new array holding the result. If the input contains integers
/// or floats smaller than float64, then the output data-type is
/// np.float64. Otherwise, the data-type of the output is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public NDarray<double> median(NDarray a, int[] axis, NDarray @out = null, bool? overwrite_input = false, bool? keepdims = false)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (axis!=null) kwargs["axis"]=ToPython(axis);
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
if (keepdims!=false) kwargs["keepdims"]=ToPython(keepdims);
dynamic py = __self__.InvokeMethod("median", pyargs, kwargs);
return ToCsharp<NDarray<double>>(py);
}
/// <summary>
/// Compute the median along the specified axis.
///
/// Returns the median of the array elements.
///
/// Notes
///
/// Given a vector V of length N, the median of V is the
/// middle value of a sorted copy of V, V_sorted - i
/// e., V_sorted[(N-1)/2], when N is odd, and the average of the
/// two middle values of V_sorted when N is even.
/// </summary>
/// <param name="a">
/// Input array or object that can be converted to an array.
/// </param>
/// <param name="@out">
/// Alternative output array in which to place the result. It must
/// have the same shape and buffer length as the expected output,
/// but the type (of the output) will be cast if necessary.
/// </param>
/// <param name="overwrite_input">
/// If True, then allow use of memory of input array a for
/// calculations. The input array will be modified by the call to
/// median. This will save memory when you do not need to preserve
/// the contents of the input array. Treat the input as undefined,
/// but it will probably be fully or partially sorted. Default is
/// False. If overwrite_input is True and a is not already an
/// ndarray, an error will be raised.
/// </param>
/// <returns>
/// A new array holding the result. If the input contains integers
/// or floats smaller than float64, then the output data-type is
/// np.float64. Otherwise, the data-type of the output is the
/// same as that of the input. If out is specified, that array is
/// returned instead.
/// </returns>
public double median(NDarray a, NDarray @out = null, bool? overwrite_input = false)
{
//auto-generated code, do not change
var __self__=self;
var pyargs=ToTuple(new object[]
{
a,
});
var kwargs=new PyDict();
if (@out!=null) kwargs["out"]=ToPython(@out);
if (overwrite_input!=false) kwargs["overwrite_input"]=ToPython(overwrite_input);
dynamic py = __self__.InvokeMethod("median", pyargs, kwargs);
return ToCsharp<double>(py);
}
/// <summary>
/// Compute the weighted average along the specified axis.
/// </summary>
/// <param name="a">