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Refactor code formatting and improve consistency across various test files

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- Removed unnecessary blank lines in multiple test files to enhance readability.
- Ensured consistent spacing and formatting in the `Trima`, `Usf`, `Vidya`, `Wma`, and `Atr` test classes.
- Updated comments for clarity and consistency in the `Atr` and `Adl` classes.
- Adjusted project files for better structure and maintainability.
This commit is contained in:
Miha Kralj
2025-12-28 17:44:08 -08:00
parent ad6eebf812
commit 13d7c1215d
169 changed files with 10815 additions and 10814 deletions
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lib/core/simd/SimdExtensions.Tests.cs
+15 -15
View File
@@ -113,7 +113,7 @@ public class SimdExtensionsTests
double[] data = new double[1000];
for (int i = 0; i < data.Length; i++)
data[i] = i + 1.0;
var span = new ReadOnlySpan<double>(data);
double expected = 1000.0 * 1001.0 / 2.0;
Assert.Equal(expected, span.SumSIMD(), precision: 8);
@@ -324,7 +324,7 @@ public class SimdExtensionsTests
{
double[] data = [2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0];
var span = new ReadOnlySpan<double>(data);
double variance = span.VarianceSIMD();
Assert.True(Math.Abs(variance - 4.571428) < 0.0001);
}
@@ -334,10 +334,10 @@ public class SimdExtensionsTests
{
double[] data = [2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0];
var span = new ReadOnlySpan<double>(data);
double mean = 5.0;
double variance = span.VarianceSIMD(mean);
Assert.True(variance > 0);
}
@@ -379,7 +379,7 @@ public class SimdExtensionsTests
{
double[] data = [2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0];
var span = new ReadOnlySpan<double>(data);
double stdDev = span.StdDevSIMD();
Assert.True(Math.Abs(stdDev - 2.138) < 0.01);
}
@@ -389,7 +389,7 @@ public class SimdExtensionsTests
{
double[] data = [2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0];
var span = new ReadOnlySpan<double>(data);
double stdDev = span.StdDevSIMD(5.0);
Assert.True(stdDev > 0);
}
@@ -556,14 +556,14 @@ public class SimdExtensionsTests
public void SIMD_WorksWithTSeriesValues()
{
var series = new TSeries(100);
for (int i = 0; i < 100; i++)
{
series.Add(DateTime.UtcNow.Ticks + i, i + 1.0);
}
var values = series.Values;
double sum = values.SumSIMD();
double avg = values.AverageSIMD();
double min = values.MinSIMD();
@@ -587,7 +587,7 @@ public class SimdExtensionsTests
var bars = gbm.Fetch(1000, startTime, interval);
var closeValues = bars.Close.Values;
double sum = closeValues.SumSIMD();
double avg = closeValues.AverageSIMD();
double min = closeValues.MinSIMD();
@@ -611,7 +611,7 @@ public class SimdExtensionsTests
_ = closeValues.SumSIMD();
var sw = System.Diagnostics.Stopwatch.StartNew();
double sum = closeValues.SumSIMD();
double avg = closeValues.AverageSIMD();
double min = closeValues.MinSIMD();
@@ -619,7 +619,7 @@ public class SimdExtensionsTests
var (minAlt, maxAlt) = closeValues.MinMaxSIMD();
double variance = closeValues.VarianceSIMD();
double stdDev = closeValues.StdDevSIMD();
sw.Stop();
Assert.True(sum > 0);
@@ -630,8 +630,8 @@ public class SimdExtensionsTests
Assert.Equal(max, maxAlt);
Assert.True(variance > 0);
Assert.True(stdDev > 0);
Assert.True(sw.ElapsedMilliseconds < 50,
Assert.True(sw.ElapsedMilliseconds < 50,
$"SIMD operations took {sw.ElapsedMilliseconds}ms, expected < 50ms");
}
@@ -640,12 +640,12 @@ public class SimdExtensionsTests
{
double[] data = [1.0, 2.0, 3.0];
var span = new ReadOnlySpan<double>(data);
Assert.Equal(6.0, span.SumSIMD());
Assert.Equal(1.0, span.MinSIMD());
Assert.Equal(3.0, span.MaxSIMD());
Assert.Equal(2.0, span.AverageSIMD());
var (min, max) = span.MinMaxSIMD();
Assert.Equal(1.0, min);
Assert.Equal(3.0, max);
lib/core/simd/SimdExtensions.md
+52 -52
View File
@@ -1,52 +1,52 @@
# SimdExtensions Class
`SimdExtensions` provides high-performance, SIMD-accelerated extension methods for `ReadOnlySpan<double>`. It leverages .NET's `Vector<T>` to achieve 4-8x speedups on supported hardware (AVX2, AVX-512) while automatically falling back to scalar implementations on older hardware.
## Key Features
- **Hardware Acceleration**: Uses CPU vector registers to process multiple elements in parallel.
- **Automatic Fallback**: Gracefully handles non-SIMD hardware or small arrays.
- **Zero-Allocation**: Operates directly on spans without creating new arrays.
- **Aggressive Inlining**: Methods are marked for inlining to minimize call overhead.
## Available Methods
| Method | Description |
|--------|-------------|
| `ContainsNonFinite()` | Checks if span contains any non-finite values (NaN or Infinity). |
| `SumSIMD()` | Calculates the sum of elements. |
| `MinSIMD()` | Finds the minimum value. |
| `MaxSIMD()` | Finds the maximum value. |
| `MinMaxSIMD()` | Finds both min and max in a single pass (more efficient than separate calls). |
| `AverageSIMD()` | Calculates the arithmetic mean. |
| `VarianceSIMD()` | Calculates the sample variance. |
| `StdDevSIMD()` | Calculates the sample standard deviation. |
| `DotProduct()` | Calculates the dot product of two spans. |
## Performance
On modern CPUs (e.g., Intel Core i7/i9, AMD Ryzen), these methods typically outperform standard LINQ or scalar loops by a factor of 4 to 8 for large arrays.
## Usage
```csharp
using QuanTAlib;
double[] data = { 1.0, 2.0, 3.0, 4.0, 5.0, ... };
ReadOnlySpan<double> span = data;
// Calculate sum
double sum = span.SumSIMD();
// Calculate min and max in one pass
var (min, max) = span.MinMaxSIMD();
// Calculate standard deviation
double stdDev = span.StdDevSIMD();
// Check for valid data
bool hasInvalid = span.ContainsNonFinite();
// Calculate dot product
double dot = span.DotProduct(otherSpan);
```
# SimdExtensions Class
`SimdExtensions` provides high-performance, SIMD-accelerated extension methods for `ReadOnlySpan<double>`. It leverages .NET's `Vector<T>` to achieve 4-8x speedups on supported hardware (AVX2, AVX-512) while automatically falling back to scalar implementations on older hardware.
## Key Features
- **Hardware Acceleration**: Uses CPU vector registers to process multiple elements in parallel.
- **Automatic Fallback**: Gracefully handles non-SIMD hardware or small arrays.
- **Zero-Allocation**: Operates directly on spans without creating new arrays.
- **Aggressive Inlining**: Methods are marked for inlining to minimize call overhead.
## Available Methods
| Method | Description |
|--------|-------------|
| `ContainsNonFinite()` | Checks if span contains any non-finite values (NaN or Infinity). |
| `SumSIMD()` | Calculates the sum of elements. |
| `MinSIMD()` | Finds the minimum value. |
| `MaxSIMD()` | Finds the maximum value. |
| `MinMaxSIMD()` | Finds both min and max in a single pass (more efficient than separate calls). |
| `AverageSIMD()` | Calculates the arithmetic mean. |
| `VarianceSIMD()` | Calculates the sample variance. |
| `StdDevSIMD()` | Calculates the sample standard deviation. |
| `DotProduct()` | Calculates the dot product of two spans. |
## Performance
On modern CPUs (e.g., Intel Core i7/i9, AMD Ryzen), these methods typically outperform standard LINQ or scalar loops by a factor of 4 to 8 for large arrays.
## Usage
```csharp
using QuanTAlib;
double[] data = { 1.0, 2.0, 3.0, 4.0, 5.0, ... };
ReadOnlySpan<double> span = data;
// Calculate sum
double sum = span.SumSIMD();
// Calculate min and max in one pass
var (min, max) = span.MinMaxSIMD();
// Calculate standard deviation
double stdDev = span.StdDevSIMD();
// Check for valid data
bool hasInvalid = span.ContainsNonFinite();
// Calculate dot product
double dot = span.DotProduct(otherSpan);
```