plus1 :: [Integer] -> [Integer] -> [Integer]
plus1 (x:xs) remain
| (x==0) && (remain==1) = [1] ++ (plus1 xs (remain-1) )
| (x==1) && (remain==1) = [0] ++ (plus1 xs remain)
| (x==0) && (remain==0) = [0] ++ (plus1 xs 0)
| (x==1) && (remain==0) = [1] ++ (plus1 xs 0)
| otherwise = []
•文字“1”
中没有（Num [Integer]）的实例•在'（==）'的第二个参数中，即'1'
在'（＆amp;＆amp;）'的第二个参数中，即'（保留== 1）'
在表达式中：（x == 0）＆amp;＆amp; （保持== 1）
答案 0 :(得分：0)
您的类型签名表明remain应为:: [Integer]，但您将其与==与1进行比较，其中Integer为plus1 :: [Integer] -> Integer -> [Integer]。您的类型签名可能是private object _lock = new object();
public double CalculatePredictedRSquared()
{
double press = 0, tss = 0, press2 = 0, press1 = 0;
Vector<double> output = CreateVector.Dense(Enumerable.Range(0, 400).Select(i => Convert.ToDouble(i)).ToArray());
List<double> input1 = new List<double>(Enumerable.Range(0, 400).Select(i => Convert.ToDouble(i)));
List<double> input2 = new List<double>(Enumerable.Range(200, 400).Select(i => Convert.ToDouble(i)));
Parallel.For(0, output.Count, i =>
{
ConcurrentBag<MultipleRegressionInfo> listMRInfoBag = new ConcurrentBag<MultipleRegressionInfo>(listMRInfo);
ConcurrentBag<double> vectorArrayBag = new ConcurrentBag<double>(output);
ConcurrentBag<double[]> matrixList = new ConcurrentBag<double[]>();
lock (_lock)
{
matrixList.Add(input1.Where((v, k) => k != i).ToArray());
matrixList.Add(input2.Where((v, k) => k != i).ToArray());
}
var matrixArray2 = CreateMatrix.DenseOfColumnArrays(matrixList);
var actualResult = vectorArrayBag.ElementAt(i);
var newVectorArray = CreateVector.Dense(vectorArrayBag.Where((v, j) => j != i).ToArray());
var items = FindBestMRSolution(matrixArray2, newVectorArray);
double estimate1 = 0;
if (items != null)
{
lock (_lock)
{
var y = 0d;
var independentCount = matrixArray2.RowCount;
var dependentCount = newVectorArray.Count;
if (independentCount == dependentCount)
{
var populationCount = independentCount;
y = newVectorArray.Average();
for (int l = 0; l < matrixArray2.ColumnCount; l++)
{
var avg = matrixArray2.Column(l).Average();
y -= avg * items[l];
}
}
for (int m = 0; m < 2; m++)
{
var coefficient = items[m];
if (m == 0)
{
estimate1 += input1.ElementAt(i) * coefficient;
}
else
{
estimate1 += input2.ElementAt(i) * coefficient;
}
}
estimate1 += y;
}
}
else
{
lock (_lock)
{
estimate1 = 0;
}
}
lock (_lock)
{
press1 += Math.Pow(actualResult - estimate1, 2);
}
});
for (int i = 0; i < output.Count; i++)
{
List<double[]> matrixList = new List<double[]>();
matrixList.Add(input1.Where((v, k) => k != i).ToArray());
matrixList.Add(input2.Where((v, k) => k != i).ToArray());
var matrixArray = CreateMatrix.DenseOfColumnArrays(matrixList);
var actualResult = output.ElementAt(i);
var newVectorArray = CreateVector.Dense(output.Where((v, j) => j != i).ToArray());
var items = FindBestMRSolution(matrixArray, newVectorArray);
double estimate = 0;
if (items != null)
{
var y = CalculateYIntercept(matrixArray, newVectorArray, items);
for (int m = 0; m < 2; m++)
{
var coefficient = items[m];
if (m == 0)
{
estimate += input1.ElementAt(i) * coefficient;
}
else
{
estimate += input2.ElementAt(i) * coefficient;
}
}
}
else
{
estimate = 0;
}
press2 += Math.Pow(actualResult - estimate, 2);
}
tss = CalculateTotalSumOfSquares(vectorArray.ToList());
var test1 = 1 - (press1 / tss);
var test2 = 1 - (press2 / tss);
}
public Vector<double> CalculateWithQR(Matrix<double> x, Vector<double> y)
{
Vector<double> result = null;
result = MultipleRegression.QR(x, y);
for (int i = 0; i < result.Count; i++)
{
var value = result.ElementAt(i);
if (Double.IsNaN(value) || Double.IsInfinity(value))
{
return null;
}
}
return result;
}
public Vector<double> CalculateWithNormal(Matrix<double> x, Vector<double> y)
{
Vector<double> result = null;
result = MultipleRegression.NormalEquations(x, y);
for (int i = 0; i < result.Count; i++)
{
var value = result.ElementAt(i);
if (Double.IsNaN(value) || Double.IsInfinity(value))
{
return null;
}
}
return result;
}
public Vector<double> CalculateWithSVD(Matrix<double> x, Vector<double> y)
{
Vector<double> result = null;
result = MultipleRegression.Svd(x, y);
for (int i = 0; i < result.Count; i++)
{
var value = result.ElementAt(i);
if (Double.IsNaN(value) || Double.IsInfinity(value))
{
return null;
}
}
return result;
}
public Vector<double> FindBestMRSolution(Matrix<double> x, Vector<double> y)
{
Vector<double> result = null;
result = CalculateWithNormal(x, y);
if (result != null)
{
return result;
}
else
{
result = CalculateWithSVD(x, y);
if (result != null)
{
return result;
}
else
{
result = CalculateWithQR(x, y);
if (result != null)
{
return result;
}
}
}
return result;
}
public double CalculateTotalSumOfSquares(List<double> dependentVariables)
{
double tts = 0;
for (int i = 0; i < dependentVariables.Count; i++)
{
tts += Math.Pow(dependentVariables.ElementAt(i) - dependentVariables.Average(), 2);
}
return tts;
}