Matrix manipulation i Python
I python kan matrix implementeres som 2D-liste eller 2D-array. Dannelse af matrix fra sidstnævnte giver de yderligere funktionaliteter til at udføre forskellige operationer i matrix. Disse operationer og array er defineret i modulet nusset .
Operation på Matrix:
- 1. add() :- Denne funktion bruges til at udføre element wise matrix addition . 2. subtract() :- Denne funktion bruges til at udføre element wise matrix subtraktion . 3. divide() :- Denne funktion bruges til at udføre element wise matrix division .
Implementering:
Python
# Python code to demonstrate matrix operations> # add(), subtract() and divide()> > # importing numpy for matrix operations> import> numpy> > # initializing matrices> x> => numpy.array([[> 1> ,> 2> ], [> 4> ,> 5> ]])> y> => numpy.array([[> 7> ,> 8> ], [> 9> ,> 10> ]])> > # using add() to add matrices> print> (> 'The element wise addition of matrix is : '> )> print> (numpy.add(x,y))> > # using subtract() to subtract matrices> print> (> 'The element wise subtraction of matrix is : '> )> print> (numpy.subtract(x,y))> > # using divide() to divide matrices> print> (> 'The element wise division of matrix is : '> )> print> (numpy.divide(x,y))> |
Output:
The element wise addition of matrix is : [[ 8 10] [13 15]] The element wise subtraction of matrix is : [[-6 -6] [-5 -5]] The element wise division of matrix is : [[ 0.14285714 0.25 ] [ 0.44444444 0.5 ]]
- 4. multiplicere() :- Denne funktion bruges til at udføre element wise matrix multiplikation . 5. dot():- Denne funktion bruges til at beregne matrix multiplikation, snarere end elementvis multiplikation .
Python
# Python code to demonstrate matrix operations> # multiply() and dot()> > # importing numpy for matrix operations> import> numpy> > # initializing matrices> x> => numpy.array([[> 1> ,> 2> ], [> 4> ,> 5> ]])> y> => numpy.array([[> 7> ,> 8> ], [> 9> ,> 10> ]])> > # using multiply() to multiply matrices element wise> print> (> 'The element wise multiplication of matrix is : '> )> print> (numpy.multiply(x,y))> > # using dot() to multiply matrices> print> (> 'The product of matrices is : '> )> print> (numpy.dot(x,y))> |
Output:
The element wise multiplication of matrix is : [[ 7 16] [36 50]] The product of matrices is : [[25 28] [73 82]]
- 6. sqrt():- Denne funktion bruges til at beregne kvadratroden af hvert element af matrix. 7. sum(x,akse):- Denne funktion bruges til tilføje alle elementer i matrix . Valgfrit akse-argument beregner kolonnesum, hvis aksen er 0 og rækkesum hvis aksen er 1 . 8. T :- Dette argument er vant til omsætte den angivne matrix.
Implementering:
Python
# Python code to demonstrate matrix operations> # sqrt(), sum() and 'T'> > # importing numpy for matrix operations> import> numpy> > # initializing matrices> x> => numpy.array([[> 1> ,> 2> ], [> 4> ,> 5> ]])> y> => numpy.array([[> 7> ,> 8> ], [> 9> ,> 10> ]])> > # using sqrt() to print the square root of matrix> print> (> 'The element wise square root is : '> )> print> (numpy.sqrt(x))> > # using sum() to print summation of all elements of matrix> print> (> 'The summation of all matrix element is : '> )> print> (numpy.> sum> (y))> > # using sum(axis=0) to print summation of all columns of matrix> print> (> 'The column wise summation of all matrix is : '> )> print> (numpy.> sum> (y,axis> => 0> ))> > # using sum(axis=1) to print summation of all columns of matrix> print> (> 'The row wise summation of all matrix is : '> )> print> (numpy.> sum> (y,axis> => 1> ))> > # using 'T' to transpose the matrix> print> (> 'The transpose of given matrix is : '> )> print> (x.T)> |
Output:
The element wise square root is : [[ 1. 1.41421356] [ 2. 2.23606798]] The summation of all matrix element is : 34 The column wise summation of all matrix is : [16 18] The row wise summation of all matrix is : [15 19] The transpose of given matrix is : [[1 4] [2 5]]
Brug af indlejrede løkker:
Nærme sig:
- Definer matricer A og B.
- Få antallet af rækker og kolonner i matricerne ved hjælp af len()-funktionen.
- Initialiser matricer C, D og E med nuller ved hjælp af indlejrede sløjfer eller listeforståelse.
- Brug indlejrede sløjfer eller listeforståelse til at udføre elementmæssig addition, subtraktion og division af matricer.
- Udskriv de resulterende matricer C, D og E.
Python3
A> => [[> 1> ,> 2> ],[> 4> ,> 5> ]]> B> => [[> 7> ,> 8> ],[> 9> ,> 10> ]]> rows> => len> (A)> cols> => len> (A[> 0> ])> > # Element wise addition> C> => [[> 0> for> i> in> range> (cols)]> for> j> in> range> (rows)]> for> i> in> range> (rows):> > for> j> in> range> (cols):> > C[i][j]> => A[i][j]> +> B[i][j]> print> (> 'Addition of matrices:
'> , C)> > # Element wise subtraction> D> => [[> 0> for> i> in> range> (cols)]> for> j> in> range> (rows)]> for> i> in> range> (rows):> > for> j> in> range> (cols):> > D[i][j]> => A[i][j]> -> B[i][j]> print> (> 'Subtraction of matrices:
'> , D)> > # Element wise division> E> => [[> 0> for> i> in> range> (cols)]> for> j> in> range> (rows)]> for> i> in> range> (rows):> > for> j> in> range> (cols):> > E[i][j]> => A[i][j]> /> B[i][j]> print> (> 'Division of matrices:
'> , E)> |
Produktion
Addition of matrices: [[8, 10], [13, 15]] Subtraction of matrices: [[-6, -6], [-5, -5]] Division of matrices: [[0.14285714285714285, 0.25], [0.4444444444444444, 0.5]]
Tidskompleksitet: O(n^2)
Rumkompleksitet: O(n^2)
