Python matricu var ieviest kā 2D sarakstu vai 2D masīvu. Veidojot matricu no pēdējās, tiek dota papildu funkcionalitāte dažādu operāciju veikšanai matricā. Šīs operācijas un masīvs ir definēti modulī nejutīgs .
Darbība uz Matrix:
- 1. add() :- Šī funkcija tiek izmantota, lai veiktu elementu matricas pievienošana . 2. subtract() :- Šī funkcija tiek izmantota, lai veiktu elementu viedās matricas atņemšana . 3. divide() :- Šī funkcija tiek izmantota, lai veiktu elementu viedās matricas dalījums .
Īstenošana:
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))> |
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Izvade:
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. multiply() :- Šī funkcija tiek izmantota, lai veiktu elementu viedā matricas reizināšana . 5. dot() :- Šī funkcija tiek izmantota, lai aprēķinātu matricas reizināšanu, nevis elementu reizināšanu .
Python
kā java pārvērst virkni par veselu skaitli
# 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))> |
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Izvade:
The element wise multiplication of matrix is : [[ 7 16] [36 50]] The product of matrices is : [[25 28] [73 82]]>
- 6. sqrt() :- Šī funkcija tiek izmantota, lai aprēķinātu katra elementa kvadrātsakne no matricas. 7. summa(x,axis):- Šī funkcija tiek izmantota pievienojiet visus elementus matricā . Izvēles ass arguments aprēķina kolonnas summa, ja ass ir 0 un rindas summa, ja ass ir 1 . 8. T :- Šis arguments ir pieradis transponēt norādītā matrica.
Īstenošana:
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)> |
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Izvade:
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]]>
Izmantojot ligzdotas cilpas:
Pieeja:
- Definējiet matricas A un B.
- Iegūstiet matricu rindu un kolonnu skaitu, izmantojot funkciju len().
- Inicializējiet matricas C, D un E ar nullēm, izmantojot ligzdotas cilpas vai saraksta izpratni.
- Izmantojiet ligzdotās cilpas vai saraksta izpratni, lai veiktu matricu saskaitīšanu, atņemšanu un dalīšanu pa elementiem.
- Izdrukājiet iegūtās matricas C, D un E.
Python3
java nomaiņa
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)> |
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>Izvade
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]]>
Laika sarežģītība: O(n^2)
Telpas sarežģītība: O(n^2)