repl.txt (4319B)
1 2 {{alias}}( N, correction, x, stride ) 3 Computes the standard deviation of a single-precision floating-point strided 4 array ignoring `NaN` values and using a one-pass algorithm proposed by 5 Youngs and Cramer. 6 7 The `N` and `stride` parameters determine which elements in `x` are accessed 8 at runtime. 9 10 Indexing is relative to the first index. To introduce an offset, use a typed 11 array view. 12 13 If `N <= 0`, the function returns `NaN`. 14 15 If every indexed element is `NaN`, the function returns `NaN`. 16 17 Parameters 18 ---------- 19 N: integer 20 Number of indexed elements. 21 22 correction: number 23 Degrees of freedom adjustment. Setting this parameter to a value other 24 than `0` has the effect of adjusting the divisor during the calculation 25 of the standard deviation according to `N - c` where `c` corresponds to 26 the provided degrees of freedom adjustment. When computing the standard 27 deviation of a population, setting this parameter to `0` is the standard 28 choice (i.e., the provided array contains data constituting an entire 29 population). When computing the corrected sample standard deviation, 30 setting this parameter to `1` is the standard choice (i.e., the provided 31 array contains data sampled from a larger population; this is commonly 32 referred to as Bessel's correction). 33 34 x: Float32Array 35 Input array. 36 37 stride: integer 38 Index increment. 39 40 Returns 41 ------- 42 out: number 43 The standard deviation. 44 45 Examples 46 -------- 47 // Standard Usage: 48 > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, -2.0, NaN, 2.0 ] ); 49 > {{alias}}( x.length, 1, x, 1 ) 50 ~2.0817 51 52 // Using `N` and `stride` parameters: 53 > x = new {{alias:@stdlib/array/float32}}( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] ); 54 > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 ); 55 > var stride = 2; 56 > {{alias}}( N, 1, x, stride ) 57 ~2.0817 58 59 // Using view offsets: 60 > var x0 = new {{alias:@stdlib/array/float32}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] ); 61 > var x1 = new {{alias:@stdlib/array/float32}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); 62 > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 ); 63 > stride = 2; 64 > {{alias}}( N, 1, x1, stride ) 65 ~2.0817 66 67 {{alias}}.ndarray( N, correction, x, stride, offset ) 68 Computes the standard deviation of a single-precision floating-point strided 69 array ignoring `NaN` values and using a one-pass algorithm proposed by 70 Youngs and Cramer and alternative indexing semantics. 71 72 While typed array views mandate a view offset based on the underlying 73 buffer, the `offset` parameter supports indexing semantics based on a 74 starting index. 75 76 Parameters 77 ---------- 78 N: integer 79 Number of indexed elements. 80 81 correction: number 82 Degrees of freedom adjustment. Setting this parameter to a value other 83 than `0` has the effect of adjusting the divisor during the calculation 84 of the standard deviation according to `N - c` where `c` corresponds to 85 the provided degrees of freedom adjustment. When computing the standard 86 deviation of a population, setting this parameter to `0` is the standard 87 choice (i.e., the provided array contains data constituting an entire 88 population). When computing the corrected sample standard deviation, 89 setting this parameter to `1` is the standard choice (i.e., the provided 90 array contains data sampled from a larger population; this is commonly 91 referred to as Bessel's correction). 92 93 x: Float32Array 94 Input array. 95 96 stride: integer 97 Index increment. 98 99 offset: integer 100 Starting index. 101 102 Returns 103 ------- 104 out: number 105 The standard deviation. 106 107 Examples 108 -------- 109 // Standard Usage: 110 > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, -2.0, NaN, 2.0 ] ); 111 > {{alias}}.ndarray( x.length, 1, x, 1, 0 ) 112 ~2.0817 113 114 // Using offset parameter: 115 > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] ); 116 > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 ); 117 > {{alias}}.ndarray( N, 1, x, 2, 1 ) 118 ~2.0817 119 120 See Also 121 -------- 122