本文整理汇总了C#中Vector.Distinct方法的典型用法代码示例。如果您正苦于以下问题：C# Vector.Distinct方法的具体用法？C# Vector.Distinct怎么用？C# Vector.Distinct使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Vector的用法示例。
在下文中一共展示了Vector.Distinct方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Calculate
internal override double Calculate(Vector x)
{
if (x == null)
throw new InvalidOperationException("x does not exist!");
var px = (from i in x.Distinct()
let q = (from j in x
where j == i
select j).Count()
select (q / (double)x.Length)).Max();
return System.Math.Round(1 - px, 4);
}示例2: Calculate
internal override double Calculate(Vector x)
{
if (x == null)
throw new InvalidOperationException("x does not exist!");
var px = (from i in x.Distinct()
let q = (from j in x
where j == i
select j).Count()
select q / (double)x.Length);
double imp = (from p in px
select -1 * p * System.Math.Log(p, 2)).Sum();
// rouding off to 4 sig figs...
return System.Math.Round(imp, 4);
}示例3: Calculate
internal override double Calculate(Vector x)
{
if (x == null)
throw new InvalidOperationException("x does not exist!");
var px = (from i in x.Distinct()
let q = (from j in x
where j == i
select j).Count()
select (q / (double)x.Length)).ToArray();
var g = 1 - (from j in px
select j * j).Sum();
// thought this was the right one...
//for (int i = 0; i < px.Length; i++)
// for (int j = i + 1; j < px.Length; j++)
// imp += px[i] * px[j];
return System.Math.Round(g, 4); ;
}示例4: CalculateConditional
internal double CalculateConditional(Vector y, Vector x)
{
if (x == null && y == null)
throw new InvalidOperationException("x and y do not exist!");
var values = x.Distinct();
var valueCount = values.Count();
double result = 0;
// binary split
if (Width == 2)
{
// do binary split based on mean
var mean = x.Mean();
var ltSlice = y.Slice(x.Indices(d => d < mean));
var gtSlice = y.Slice(x.Indices(d => d >= mean));
var lh = Calculate(ltSlice);
var rh = Calculate(gtSlice);
result = (0.5 * lh) + (0.5 * rh);
SplitValues = new double[] { x.Min(), mean, x.Max() + 1 };
}
// if valueCount has more values than
// width allows, segment out data to
// fit width
else if (valueCount > Width)
{
double p = 0;
double h = 0;
// create segmentation
// should really consider
// some kind of distribution
// for doing segmentation..
SplitValues = x.Segment(Width);
// for convenience ;)
SplitValues[SplitValues.Length - 1] += 1;
for (int i = 1; i < SplitValues.Length; i++)
{
// get slice
var s = x.Indices(d => d >= SplitValues[i - 1] && d < SplitValues[i]);
// probability of slice
p = s.Count() / (double)x.Length;
// Impurity (y | x_i)
h = Calculate(y.Slice(s));
// sum up
result += p * h;
}
}
// otherwise perform regular counting
// exercises to find cond entropy
else
{
double p = 0;
double h = 0;
// says there is no width
// since deterministic
// slice values
_width = 0;
SplitValues = new double[valueCount];
int k = -1;
// each disting x value
foreach (var i in values)
{
SplitValues[++k] = i;
// get slice
var s = x.Indices(d => d == i);
// probability of x_i
p = s.Count() / (double)x.Length;
// Impurity (y | x_i)
h = Calculate(y.Slice(s));
// sum up
result += p * h;
}
}
return result;
}