aboutsummaryrefslogtreecommitdiff
path: root/doc/nerv_matrix.md
blob: ef5118ef2dd1f5977220aa4e4f10b60c035c5d14 (plain) (blame)
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
#The Nerv Matrix Package#
Part of the [Nerv](../README.md) toolkit.

##Description##
###Underlying structure###
In the begining is could be useful to know something about the underlying structure of a __Nerv__ matrix. Please keep in mind that matrice in __Nerv__ is row-major.  
Every matrix object is a encapsulation of a C struct that describes the attributes of this matrix.  
```
typedef struct Matrix {
    size_t stride;              /* size of a row */
    long ncol, nrow, nmax;    /* dimension of the matrix, nmax is simply nrow * ncol */
    union {
        float *f;
        double *d;
        long *i;
    } data;                   /* pointer to actual storage */
    long *data_ref;
} Matrix;
```
It is worth mentioning that that `data_ref` is a counter which counts the number of references to its memory space, mind that it will also be increased when a row of the matrix is referenced(`col = m[2]`). A __Nerv__ matrix will deallocate its space when this counter is decreased to zero.
Also note that all assigning operation in __Nerv__ is reference copy, you can use `copy_tod` or `copy_toh` method to copy value. Also, row assigning operations like `m1[2]=m2[3]` is forbidden in __Nerv__.

###Class hierarchy###
The class hierarchy of the matrix classes can be clearly observed in `matrix/init.c`.
First there is a abstract base class __Nerv.Matrix__, which is inherited by __Nerv.CuMatrix__ and __Nerv.MMatrix__(also abstract).  
Finally, there is __Nerv.CuMatrixFloat__, __Nerv.CuMatrixDouble__, inheriting __Nerv.CuMatrix__, and __Nerv.MMatrixFloat__, __Nerv.MMatrixDouble__, __Nerv.MMatrixInt__ , inheriting __Nerv.MMatrix__.

##Methods##
Mind that usually a matrix object can only do calculation with matrix of its own type(a __Nerv.CuMatrixFloat__ matrix can only do add operation with a __Nerv.CuMatrixFloat__).  
In the methods description below, __Matrix__ could be __Nerv.CuMatrixFloat__, __Nerv.CuMatrixDouble__, __Nerv.MMatrixFloat__ or __Nerv.MMatrixDouble__. __Element_type__ could be `float` or `double`, respectively.
* __Matrix = Matrix(int nrow, int ncol)__  
Returns a __Matrix__ object of `nrow` rows and `ncol` columns.
* __Element_type = Matrix.get_elem(Matrix self, int index)__  
Returns the element value at the specific index(treating the matrix as a vector). The index should be less than `nmax` of the matrix.
* __void Matrix.set_elem(Matrix self, int index, Element_type value)__  
Set the value at `index` to be `value`.
* __int Matrix.ncol(Matrix self)__  
Get `ncol`, the number of columns.
* __int Matrix.nrow(Matrix self)__  
Get `nrow`, the number of rows.
* __int Matrix.get_dataref_value(Matrix self)__  
Returns the value(not a pointer) of space the `data_ref` pointer pointed to. This function is mainly for debugging.  
* __Matrix/Element\_type, boolean Matrix.\_\_index\_\_(Matrix self, int index)__  
If the matrix has more than one row, will return the row at `index` as a __Matrix__ . Otherwise it will return the value at `index`.
* __void Matrix.\_\_newindex\_\_(Matrix self, int index, Element_type value)__  
Set the element at `index` to be `value`.
---
* __Matrix Matrix.create(Matrix a)__  
Return a new __Matrix__ of `a`'s size(of the same number of rows and columns).
* __Matrix Matrix.colsum(Matrix self)__  
Return a new __Matrix__ of size (1,`self.ncol`), which stores the sum of all columns of __Matrix__ `self`.
* __Matrix Matrix.rowsum(Matrix self)__  
Return a new __Matrix__ of size (`self.nrow`,1), which stores the sum of all rows of __Matrix__ `self`.
* __Matrix Matrix.rowmax(Matrix self)__  
Return a new __Matrix__ of size (`self.nrow`,1), which stores the max value of all rows of __Matrix__ `self`.
* __Matrix Matrix.trans(Matrix self)__  
Return a new __Matrix__ of size (`self.ncol`,`self.nrow`), which stores the transpose of __Matrix__ `self`.
* __void Matrix.copy_fromh(Matrix self, MMatrix a)__  
Copy the content of a __MMatrix__ `a` to __Matrix__ `self`, they should be of the same size.
* __void Matrix.copy_fromd(Matrix self, CuMatrix a)__  
Copy the content of a __CuMatrix__ `a` to __Matrix__ `self`, they should be of the same size.
* __void Matrix.copy_toh(Matrix self, MMatrix a)__  
Copy the content of the __Matrix__ `self` to a __MMatrix__ `a`.
* __void Matrix.copy_tod(Matrix self, CuMatrix a)__  
Copy the content of the __Matrix__ `self` to a __CuMatrix__ `a`.
* __void Matrix.add(Matrix self, Matrix ma, Matrix mb, Element_type alpha, Element_type beta)__  
It sets the content of __Matrix__ `self` to be `alpha * ma + beta * mb`.__Matrix__ `ma,mb,self` should be of the same size.
* __void Matrix.mul(Matrix self, Matrix ma, Matrix mb, Element_type alpha, Element_type beta, [string ta, string tb])__  
It sets the content of __Matrix__ `self` to be `beta * self + alpha * ma * mb`. `ta` and `tb` is optional, if `ta` is 'T', then `ma` will be transposed, also if `tb` is 'T', `mb` will be transposed.
* __void Matrix.add_row(Matrix self, Matrix va, Element_type beta)__  
Add `beta * va` to every row of __Matrix__ `self`.
* __void Matrix.fill(Matrix self, Element_type value)__  
Fill the content of __Matrix__ `self` to be `value`.
* __void Matrix.sigmoid(Matrix self, Matrix ma)__  
Set the element of __Matrix__ `self` to be elementwise-sigmoid of `ma`.
* __void Matrix.sigmoid_grad(Matrix self, Matrix err, Matrix output)__  
Set the element of __Matrix__ `self`, to be `self[i][j]=err[i][j]*output[i][j]*(1-output[i][j])`. This function is used to propagate sigmoid layer error.
* __void Matrix.softmax(Matrix self, Matrix a)__  
Calculate a row-by-row softmax of __Matrix__ `a` and save the result in `self`.
* __void Matrix.mul_elem(Matrix self, Matrix ma, Matrix mb)__  
Calculate element-wise multiplication of __Matrix__ `ma` and `mb`, store the result in `self`.
* __void Matrix.log_elem(Matrix self, Matrix ma)__  
Calculate element-wise log of __Matrix__ `ma`, store the result in `self`.
* __void Matrix.copy_rows_fromh_by_idx(Matrix self, MMatrix ma, MMatrixInt idx)__  
`idx` should be a row vector. This function copy the rows of `ma` to `self` according to `idx`, in other words, it assigns `ma[idx[i]]` to `self[i]`.