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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.
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Miha Kralj
2025-12-28 17:44:08 -08:00
parent ad6eebf812
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lib/core/simd/SimdExtensions.md
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# 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);
```