ndims(a) |
ndim(a) 또는 a.ndim |
A (텐서 / 순위) 차원을 얻기 |
|
numel(a) |
size(a) 또는 a.size |
배열 요소 수를 가져 |
|
size(a) |
shape(a) 또는 a.shape |
행렬의 "크기"를 얻을 |
|
size(a,n) |
a.shape[n-1] |
의 요소의 개수 얻을 N 어레이 (A)의 사이즈를 제. (파이썬은 0 기반 인덱스를 사용하는 반면 MATLAB® 1 기반 색인을 사용합니다 노트 '색인'참조 ) |
|
[ 1 2 3; 4 5 6 ] |
array([[1.,2.,3.], [4.,5.,6.]]) |
mat([[1.,2.,3.], [4.,5.,6.]]) 또는 mat("1 2 3; 4 5 6") |
2 × 3 행렬을 그대로 |
[ a b; c d ] |
vstack([hstack([a,b]), hstack([c,d])]) |
bmat('a b; c d') |
블록 A, B, C 및 D에서, 매트릭스를 구성 |
a(end) |
a[-1] |
a[:,-1][0,0] |
1xn 행렬 액세스 마지막 요소 a |
a(2,5) |
a[1,4] |
둘째 행에 접속 소자, 열 다섯 번째 |
|
a(2,:) |
a[1] 또는 a[1,:] |
전체의 두 번째 행 a |
|
a(1:5,:) |
a[0:5]또는 a[:5]또는a[0:5,:] |
처음 5 행 a |
|
a(end-4:end,:) |
a[-5:] |
의 마지막 5 행 a |
|
a(1:3,5:9) |
a[0:3][:,4:9] |
행 9-3에 하나 열 다섯 a. 이 읽기 전용 부여합니다 액세스. |
|
a([2,4,5],[1,3]) |
a[ix_([1,3,4],[0,2])] |
행 2,4 및 5 열 1, 3이 행렬이 변경 될 수 있으며, 일정한 슬라이스를 필요로하지 않는다. |
|
a(3:2:21,:) |
a[ 2:21:2,:] |
다른 모든 행 a, 세 번째에서 시작하여 21로 이동 |
|
a(1:2:end,:) |
a[ ::2,:] |
다른 모든 행 a첫 번째로 시작, |
|
a(end:-1:1,:) 또는 flipud(a) |
a[ ::-1,:] |
a 역순 행 |
|
a([1:end 1],:) |
a[r_[:len(a),0]] |
a 말미에 첨부 된 첫번째 행의 복사본 |
|
a.' |
a.transpose() 또는 a.T |
의 전치 a |
|
a' |
a.conj().transpose() 또는 a.conj().T |
a.H |
의 켤레 전치 a |
a * b |
dot(a,b) |
a * b |
행렬 곱셈 |
a .* b |
a * b |
multiply(a,b) |
요소 현명한 곱셈 |
a./b |
a/b |
분할 요소 와이즈 |
|
a.^3 |
a**3 |
power(a,3) |
요소 현명한 지수 |
(a>0.5) |
(a>0.5) |
행렬 I는 j 번째 원소 (a_ij> 0.5) |
|
find(a>0.5) |
nonzero(a>0.5) |
find the indices where (a > 0.5) |
|
a(:,find(v>0.5)) |
a[:,nonzero(v>0.5)[0]] |
a[:,nonzero(v.A>0.5)[0]] |
extract the columms of a where vector v > 0.5 |
a(:,find(v>0.5)) |
a[:,v.T>0.5] |
a[:,v.T>0.5)] |
extract the columms of a where column vector v > 0.5 |
a(a<0.5)=0 |
a[a<0.5]=0 |
a with elements less than 0.5 zeroed out |
|
a .* (a>0.5) |
a * (a>0.5) |
mat(a.A * (a>0.5).A) |
a with elements less than 0.5 zeroed out |
a(:) = 3 |
a[:] = 3 |
set all values to the same scalar value |
|
y=x |
y = x.copy() |
numpy assigns by reference |
|
y=x(2,:) |
y = x[1,:].copy() |
numpy slices are by reference |
|
y=x(:) |
y = x.flatten(1) |
turn array into vector (note that this forces a copy) |
|
1:10 |
arange(1.,11.) or r_[1.:11.] or r_[1:10:10j] |
mat(arange(1.,11.)) or r_[1.:11.,'r'] |
create an increasing vector see note ‘RANGES’ |
0:9 |
arange(10.) or r_[:10.] or r_[:9:10j] |
mat(arange(10.)) or r_[:10.,'r'] |
create an increasing vector see note ‘RANGES’ |
[1:10]' |
arange(1.,11.)[:, newaxis] |
r_[1.:11.,'c'] |
create a column vector |
zeros(3,4) |
zeros((3,4)) |
mat(...) |
3x4 rank-2 array full of 64-bit floating point zeros |
zeros(3,4,5) |
zeros((3,4,5)) |
mat(...) |
3x4x5 rank-3 array full of 64-bit floating point zeros |
ones(3,4) |
ones((3,4)) |
mat(...) |
3x4 rank-2 array full of 64-bit floating point ones |
eye(3) |
eye(3) |
mat(...) |
3x3 identity matrix |
diag(a) |
diag(a) |
mat(...) |
vector of diagonal elements of a |
diag(a,0) |
diag(a,0) |
mat(...) |
square diagonal matrix whose nonzero values are the elements of a |
rand(3,4) |
random.rand(3,4) |
mat(...) |
random 3x4 matrix |
linspace(1,3,4) |
linspace(1,3,4) |
mat(...) |
4 equally spaced samples between 1 and 3, inclusive |
[x,y]=meshgrid(0:8,0:5) |
mgrid[0:9.,0:6.] or meshgrid(r_[0:9.],r_[0:6.] |
mat(...) |
two 2D arrays: one of x values, the other of y values |
|
ogrid[0:9.,0:6.] or ix_(r_[0:9.],r_[0:6.] |
mat(...) |
the best way to eval functions on a grid |
[x,y]=meshgrid([1,2,4],[2,4,5]) |
meshgrid([1,2,4],[2,4,5]) |
mat(...) |
|
|
ix_([1,2,4],[2,4,5]) |
mat(...) |
the best way to eval functions on a grid |
repmat(a, m, n) |
tile(a, (m, n)) |
mat(...) |
create m by n copies of a |
[a b] |
concatenate((a,b),1) or hstack((a,b))or column_stack((a,b)) or c_[a,b] |
concatenate((a,b),1) |
concatenate columns of a and b |
[a; b] |
concatenate((a,b)) or vstack((a,b))or r_[a,b] |
concatenate((a,b)) |
concatenate rows of a and b |
max(max(a)) |
a.max() |
maximum element of a (with ndims(a)<=2 for matlab) |
|
max(a) |
a.max(0) |
maximum element of each column of matrix a |
|
max(a,[],2) |
a.max(1) |
maximum element of each row of matrix a |
|
max(a,b) |
maximum(a, b) |
compares a and b element-wise, and returns the maximum value from each pair |
|
norm(v) |
sqrt(dot(v,v)) or Sci.linalg.norm(v) or linalg.norm(v) |
sqrt(dot(v.A,v.A)) or Sci.linalg.norm(v)or linalg.norm(v) |
L2 norm of vector v |
a & b |
logical_and(a,b) |
element-by-element AND operator (Numpy ufunc) see note ‘LOGICOPS’ |
|
a | b |
logical_or(a,b) |
element-by-element OR operator (Numpy ufunc) see note ‘LOGICOPS’ |
|
bitand(a,b) |
a & b |
bitwise AND operator (Python native and Numpy ufunc) |
|
bitor(a,b) |
a | b |
bitwise OR operator (Python native and Numpy ufunc) |
|
inv(a) |
linalg.inv(a) |
inverse of square matrix a |
|
pinv(a) |
linalg.pinv(a) |
pseudo-inverse of matrix a |
|
rank(a) |
linalg.matrix_rank(a) |
rank of a matrix a |
|
a\b |
linalg.solve(a,b) if a is square linalg.lstsq(a,b) otherwise |
solution of a x = b for x |
|
b/a |
Solve a.T x.T = b.T instead |
solution of x a = b for x |
|
[U,S,V]=svd(a) |
U, S, Vh = linalg.svd(a), V = Vh.T |
singular value decomposition of a |
|
chol(a) |
linalg.cholesky(a).T |
cholesky factorization of a matrix (chol(a) in matlab returns an upper triangular matrix, but linalg.cholesky(a) returns a lower triangular matrix) |
|
[V,D]=eig(a) |
D,V = linalg.eig(a) |
eigenvalues and eigenvectors of a |
|
[V,D]=eig(a,b) |
V,D = Sci.linalg.eig(a,b) |
eigenvalues and eigenvectors of a,b |
|
[V,D]=eigs(a,k) |
|
|
find the k largest eigenvalues and eigenvectors of a |
[Q,R,P]=qr(a,0) |
Q,R = Sci.linalg.qr(a) |
mat(...) |
QR decomposition |
[L,U,P]=lu(a) |
L,U = Sci.linalg.lu(a) or LU,P=Sci.linalg.lu_factor(a) |
mat(...) |
LU decomposition (note: P(Matlab) == transpose(P(numpy)) ) |
conjgrad |
Sci.linalg.cg |
mat(...) |
Conjugate gradients solver |
fft(a) |
fft(a) |
mat(...) |
Fourier transform of a |
ifft(a) |
ifft(a) |
mat(...) |
inverse Fourier transform of a |
sort(a) |
sort(a) or a.sort() |
mat(...) |
sort the matrix |
[b,I] = sortrows(a,i) |
I = argsort(a[:,i]), b=a[I,:] |
sort the rows of the matrix |
|
regress(y,X) |
linalg.lstsq(X,y) |
multilinear regression |
|
decimate(x, q) |
Sci.signal.resample(x, len(x)/q) |
downsample with low-pass filtering |
|
unique(a) |
unique(a) |
|
|
squeeze(a) |
a.squeeze() |
|
|