jiro_nn 0.8.1

Neural Networks framework with model building & data preprocessing features.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164

pub enum Backends {
    ArrayFire,
    Nalgebra,
    Ndarray
}

#[cfg(feature = "f64")]
pub type Scalar = Scalar;

#[cfg(not(feature = "f64"))]
pub type Scalar = f32;

#[cfg(feature = "arrayfire")]
pub mod arrayfire_matrix;

#[cfg(feature = "arrayfire")]
pub use arrayfire_matrix::Matrix;
#[cfg(feature = "arrayfire")]
pub const BACKEND: Backends = Backends::ArrayFire;

#[cfg(feature = "nalgebra")]
pub mod nalgebra_matrix;

#[cfg(all(feature = "nalgebra", not(feature = "arrayfire")))]
pub use nalgebra_matrix::Matrix;
#[cfg(all(feature = "nalgebra", not(feature = "arrayfire")))]
pub const BACKEND: Backends = Backends::Nalgebra;

#[cfg(feature = "ndarray")]
pub mod ndarray_matrix;

#[cfg(all(feature = "ndarray", not(feature = "arrayfire"), not(feature = "nalgebra")))]
pub use ndarray_matrix::Matrix;
#[cfg(all(feature = "ndarray", not(feature = "arrayfire"), not(feature = "nalgebra")))]
pub const BACKEND: Backends = Backends::Ndarray;

pub trait MatrixTrait: Clone {
    fn is_backend_thread_safe() -> bool;

    fn zeros(nrow: usize, ncol: usize) -> Self;

    fn constant(nrow: usize, ncol: usize, value: Scalar) -> Self;

    fn identity(n: usize) -> Self;

    /// Creates a matrix with random values between min and max (excluded).
    fn random_uniform(nrow: usize, ncol: usize, min: Scalar, max: Scalar) -> Self;

    /// Creates a matrix with random values following a normal distribution.
    fn random_normal(nrow: usize, ncol: usize, mean: Scalar, std_dev: Scalar) -> Self;

    /// Fills the matrix with the iterator columns after columns by chunking the data by n_rows.
    /// ```txt
    /// Your data : [[col1: row0 row1 ... rowNrow][col2]...[colNcol]]
    /// Result :
    /// [
    ///    [col0: row0 row1 ... rowNrow],
    ///    [col1: row0 row1 ... rowNrow],
    ///     ...
    ///    [colNcol: row0 row1 ... rowNrow],
    /// ]
    /// ```
    fn from_iter(nrow: usize, ncol: usize, data: impl Iterator<Item = Scalar>) -> Self;

    /// ```txt
    /// Your data :
    /// [
    ///    [row0: col0 col1 ... colNcol],
    ///    [row1: col0 col1 ... colNcol],
    ///     ...
    ///    [rowNrow: col0 col1 ... colNcol],
    /// ]
    ///
    /// Result :
    /// [
    ///    [col0: row0 row1 ... rowNrow],
    ///    [col1: row0 row1 ... rowNrow],
    ///     ...
    ///    [colNcol: row0 row1 ... rowNrow],
    /// ]
    /// ```
    fn from_row_leading_vector2(m: &Vec<Vec<Scalar>>) -> Self;

    fn from_column_leading_vector2(m: &Vec<Vec<Scalar>>) -> Self;

    /// fills a column vector row by row with values of index 0 to v.len()
    fn from_column_vector(v: &Vec<Scalar>) -> Self;

    /// fills a row vector column by column with values of index 0 to v.len()
    fn from_row_vector(v: &Vec<Scalar>) -> Self;

    fn from_fn<F>(nrows: usize, ncols: usize, f: F) -> Self
    where
        F: FnMut(usize, usize) -> Scalar;

    fn get_column_as_matrix(&self, idx: usize) -> Self;

    fn from_column_matrices(columns: &[Self]) -> Self;

    fn columns_map(&self, f: impl Fn(usize, &Vec<Scalar>) -> Vec<Scalar>) -> Self;

    fn get_column(&self, index: usize) -> Vec<Scalar>;

    fn get_row(&self, index: usize) -> Vec<Scalar>;

    fn map(&self, f: impl Fn(Scalar) -> Scalar + Sync) -> Self;

    fn map_indexed_mut(&mut self, f: impl Fn(usize, usize, Scalar) -> Scalar + Sync) -> &mut Self;

    fn dot(&self, other: &Self) -> Self;

    fn columns_sum(&self) -> Self;

    fn transpose(&self) -> Self;

    fn get_data_col_leading(&self) -> Vec<Vec<Scalar>>;

    fn get_data_row_leading(&self) -> Vec<Vec<Scalar>>;

    /// returns the dimensions of the matrix (nrow, ncol)
    fn dim(&self) -> (usize, usize);

    fn component_mul(&self, other: &Self) -> Self;

    fn component_add(&self, other: &Self) -> Self;

    fn component_sub(&self, other: &Self) -> Self;

    fn component_div(&self, other: &Self) -> Self;

    fn scalar_add(&self, scalar: Scalar) -> Self;

    fn scalar_mul(&self, scalar: Scalar) -> Self;

    fn scalar_sub(&self, scalar: Scalar) -> Self;

    fn scalar_div(&self, scalar: Scalar) -> Self;

    fn square(&self) -> Self;

    fn sum(&self) -> Scalar;

    fn mean(&self) -> Scalar;

    fn exp(&self) -> Self;

    fn max(&self) -> Scalar;

    fn min(&self) -> Scalar;

    fn maxof(&self, other: &Self) -> Self;

    fn sign(&self) -> Self;

    fn minof(&self, other: &Self) -> Self;

    fn sqrt(&self) -> Self;

    fn log(&self) -> Self;

    fn index(&self, row: usize, col: usize) -> Scalar;

    fn index_mut(&mut self, row: usize, col: usize) -> &mut Scalar;
}