130 lines
		
	
	
		
			4.0 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			130 lines
		
	
	
		
			4.0 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
| 
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| {{alias}}( N, x, strideX, y, strideY )
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|     Computes the dot product of two single-precision floating-point vectors with
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|     extended accumulation and result.
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| 
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|     The `N`, `strideX`, and `strideY` parameters determine which elements in `x`
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|     and `y` are accessed at runtime.
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| 
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|     Indexing is relative to the first index. To introduce an offset, use a typed
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|     array view.
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| 
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|     If `N <= 0` the function returns `0.0`.
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| 
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|     Parameters
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|     ----------
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|     N: integer
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|         Number of indexed elements.
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| 
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|     x: Float32Array
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|         First input array.
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| 
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|     strideX: integer
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|         Index increment for `x`.
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| 
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|     y: Float32Array
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|         Second input array.
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| 
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|     strideY: integer
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|         Index increment for `y`.
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| 
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|     Returns
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|     -------
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|     dot: number
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|         The dot product of `x` and `y`.
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| 
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|     Examples
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|     --------
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|     // Standard usage:
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|     > var x = new {{alias:@stdlib/array/float32}}( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
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|     > var y = new {{alias:@stdlib/array/float32}}( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
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|     > var dot = {{alias}}( x.length, x, 1, y, 1 )
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|     -5.0
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| 
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|     // Strides:
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|     > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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|     > y = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
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|     > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
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|     > dot = {{alias}}( N, x, 2, y, -1 )
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|     9.0
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| 
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|     // Using view offsets:
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|     > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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|     > y = new {{alias:@stdlib/array/float32}}( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
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|     > var x1 = new {{alias:@stdlib/array/float32}}( x.buffer, x.BYTES_PER_ELEMENT*1 );
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|     > var y1 = new {{alias:@stdlib/array/float32}}( y.buffer, y.BYTES_PER_ELEMENT*3 );
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|     > N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
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|     > dot = {{alias}}( N, x1, -2, y1, 1 )
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|     128.0
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| 
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| {{alias}}.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )
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|     Computes the dot product of two single-precision floating-point vectors
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|     using alternative indexing semantics and with extended accumulation and
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|     result.
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| 
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|     While typed array views mandate a view offset based on the underlying
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|     buffer, the `offsetX` and `offsetY` parameters support indexing based on a
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|     starting index.
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| 
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|     Parameters
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|     ----------
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|     N: integer
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|         Number of indexed elements.
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| 
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|     x: Float32Array
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|         First input array.
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| 
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|     strideX: integer
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|         Index increment for `x`.
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| 
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|     offsetX: integer
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|         Starting index for `x`.
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| 
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|     y: Float32Array
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|         Second input array.
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| 
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|     strideY: integer
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|         Index increment for `y`.
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| 
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|     offsetY: integer
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|         Starting index for `y`.
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| 
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|     Returns
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|     -------
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|     dot: number
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|         The dot product of `x` and `y`.
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| 
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|     Examples
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|     --------
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|     // Standard usage:
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|     > var x = new {{alias:@stdlib/array/float32}}( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
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|     > var y = new {{alias:@stdlib/array/float32}}( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
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|     > var dot = {{alias}}.ndarray( x.length, x, 1, 0, y, 1, 0 )
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|     -5.0
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| 
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|     // Strides:
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|     > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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|     > y = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
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|     > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
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|     > dot = {{alias}}.ndarray( N, x, 2, 0, y, 2, 0 )
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|     9.0
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| 
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|     // Using offset indices:
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|     > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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|     > y = new {{alias:@stdlib/array/float32}}( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
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|     > N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
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|     > dot = {{alias}}.ndarray( N, x, -2, x.length-1, y, 1, 3 )
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|     128.0
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| 
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|     References
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|     ----------
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|     - Lawson, Charles L., Richard J. Hanson, Fred T. Krogh, and David Ronald
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|     Kincaid. 1979. "Algorithm 539: Basic Linear Algebra Subprograms for Fortran
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|     Usage [F1]." *ACM Transactions on Mathematical Software* 5 (3). New York,
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|     NY, USA: Association for Computing Machinery: 324–25.
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|     doi:10.1145/355841.355848.
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| 
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|     See Also
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|     --------
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| 
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