Matrices
- pyit2fls.T1FMatrix(matrix, t1fs)
Creating a type 1 fuzzy matrix.
Parameters
matrix : numpy (n, m, ) shaped array
The input matrix.
t1fs : T1FS
Type 1 fuzzy set describing the matrix elements.
Returns
output : numpy (n, m, ) shaped array
Returns membership degrees associated with the elements of the input matrix.
Examples
>>> matrix = random.rand(3, 3) >>> domain = linspace(0., 1., 101) >>> A = T1FS(domain, mf=gaussian_mf, params=[0.6, 0.2, 1.]) >>> t1fmatrix1 = T1FMatrix(matrix, A)
- pyit2fls.T1FMatrix_Complement(matrix)
Calculating complement of a type 1 fuzzy matrix.
Parameters
matrix : numpy (n, m, ) shaped array
The input matrix.
Returns
output : numpy (n, m, ) shaped array
Returns complement of the input matrix.
Examples
>>> matrix = random.rand(3, 3) >>> domain = linspace(0., 1., 101) >>> A = T1FS(domain, mf=gaussian_mf, params=[0.6, 0.2, 1.]) >>> t1fmatrix1 = T1FMatrix(matrix, A) >>> t1fmatrix2 = T1FMatrix_Complement(t1fmatrix1)
- pyit2fls.T1FMatrix_Intersection(m1, m2, t_norm)
Calculating intersection of two type 1 fuzzy matrices.
Parameters
m1 : numpy (n, m, ) shaped array
The first fuzzy matrix.
m2 : numpy (n, m, ) shaped array
The second fuzzy matrix.
t_norm : function
T-norm function for calculating the intersection.
Returns
output : numpy (n, m, ) shaped array
Returns intersection of the input matrices.
Examples
>>> matrix = random.rand(3, 3) >>> domain = linspace(0., 1., 101) >>> A = T1FS(domain, mf=gaussian_mf, params=[0.6, 0.2, 1.]) >>> B = T1FS(domain, mf=gaussian_mf, params=[0.4, 0.2, 1.]) >>> t1fmatrix1 = T1FMatrix(matrix, A) >>> t1fmatrix2 = T1FMatrix(matrix, B) >>> t1fmatrix3 = T1FMatrix_Intersection(t1fmatrix1, t1fmatrix2, min_t_norm)
- pyit2fls.T1FMatrix_Union(m1, m2, s_norm)
Calculating union of two type 1 fuzzy matrices.
Parameters
m1 : numpy (n, m, ) shaped array
The first fuzzy matrix.
m2 : numpy (n, m, ) shaped array
The second fuzzy matrix.
s_norm : function
S-norm function for calculating the union.
Returns
output : numpy (n, m, ) shaped array
Returns union of the input matrices.
Examples
>>> matrix = random.rand(3, 3) >>> domain = linspace(0., 1., 101) >>> A = T1FS(domain, mf=gaussian_mf, params=[0.6, 0.2, 1.]) >>> B = T1FS(domain, mf=gaussian_mf, params=[0.4, 0.2, 1.]) >>> t1fmatrix1 = T1FMatrix(matrix, A) >>> t1fmatrix2 = T1FMatrix(matrix, B) >>> t1fmatrix3 = T1FMatrix_Union(t1fmatrix1, t1fmatrix2, max_s_norm)
- pyit2fls.Minmax(m1, m2)
Calculating min-max of two type 1 fuzzy matrices.
Parameters
m1 : numpy (n, m, ) shaped array
The first fuzzy matrix.
m2 : numpy (n, m, ) shaped array
The second fuzzy matrix.
Returns
output : numpy (n, m, ) shaped array
Returns min-max of the input matrices.
Examples
>>> matrix = random.rand(3, 3) >>> domain = linspace(0., 1., 101) >>> A = T1FS(domain, mf=gaussian_mf, params=[0.6, 0.2, 1.]) >>> B = T1FS(domain, mf=gaussian_mf, params=[0.4, 0.2, 1.]) >>> t1fmatrix1 = T1FMatrix(matrix, A) >>> t1fmatrix2 = T1FMatrix(matrix, B) >>> t1fmatrix3 = Minmax(t1fmatrix1, t1fmatrix2)
- pyit2fls.Maxmin(m1, m2)
Calculating max-min of two type 1 fuzzy matrices.
Parameters
m1 : numpy (n, m, ) shaped array
The first fuzzy matrix.
m2 : numpy (n, m, ) shaped array
The second fuzzy matrix.
Returns
output : numpy (n, m, ) shaped array
Returns max-min of the input matrices.
Examples
>>> matrix = random.rand(3, 3) >>> domain = linspace(0., 1., 101) >>> A = T1FS(domain, mf=gaussian_mf, params=[0.6, 0.2, 1.]) >>> B = T1FS(domain, mf=gaussian_mf, params=[0.4, 0.2, 1.]) >>> t1fmatrix1 = T1FMatrix(matrix, A) >>> t1fmatrix2 = T1FMatrix(matrix, B) >>> t1fmatrix3 = Maxmin(t1fmatrix1, t1fmatrix2)
- pyit2fls.T1FMatrix_isNull(matrix)
Checks if all elements of the input matrix is zero.
Parameters
matrix : numpy (n, m, ) shaped array
The input fuzzy matrix.
Returns
output : boolean
Returns True if all elements are zero, else False.
- pyit2fls.T1FMatrix_isUniversal(matrix)
Checks if all elements of the input matrix is one.
Parameters
matrix : numpy (n, m, ) shaped array
The input fuzzy matrix.
Returns
output : boolean
Returns True if all elements are one, else False.
- pyit2fls.T1FSoftMatrix_Product(r, c, norm, *matrices)
Calculates soft matrix product.
- pyit2fls.T1FSoftMatrix(U, F)
Creates a soft matrix.