Python/Machine learning
[λ¨Έμ λ¬λ] νμ΄μ¬ λνμ΄(NumPy) λΌμ΄λΈλ¬λ¦¬
ICanDoHee
2022. 3. 3. 23:17
[νμ΅ μλ£] [λ¨Έμ λ¬λ κ°μ 06] νμ΄μ¬(Python) Numpy (I)
NumPy
νλ ¬μ΄λ λκ·λͺ¨ λ€μ°¨μ λ°°μ΄μ μ½κ² μ²λ¦¬ ν μ μλλ‘ μ§μνλ νμ΄μ¬μ λΌμ΄λΈλ¬λ¦¬

λνμ΄ λΌμ΄λΈλ¬λ¦¬λ‘ νλ ¬ μ°μ°μ΄ κ°λ₯νλ€.
μ΄ λ vector, matrixμ νμκ³Ό μ°¨μμ νμΈνλ κ²μ΄ νμ
A νλ ¬κ³Ό B νλ ¬μ μμ±νκ³ , μ΄λ€μ νμκ³Ό μ°¨μμ νμΈνκ³ μΆμ λ, μ΄λ₯Ό νμ΄μ¬ μ½λλ‘ μ΄λ»κ² ꡬνν κΉ?
A = 1 2 3 B = -1 -2 -3
4 5 6 -4 -5 -6
import numpy as np
A = np.array([[1,2,3,],[4,5,6]])
B = np.array([[-1,-2,-3],[-4,-5,-6]])
#matrix A, Bμ νμ μΆλ ₯
print("A.shape ==", A.shape, ", B.shape ==", B.shape)
#matrix A, B μ°¨μ μΆλ ₯
print("A.ndim ==", A.ndim, ", B.ndim ==", B.ndim)
OUTPUT>>
A.shape == (2, 3) , B.shape == (2, 3)
A.ndim == 2 , B.ndim == 2
κ·Έλ λ€λ©΄, 1μ°¨μ λ°°μ΄ vectorμ νμ μΆλ ₯νκΈ° μν΄μλ μ΄λ»κ² ν΄μΌν κΉ?
λ°λ‘, reshape() ν¨μλ₯Ό μ¬μ©ν΄μ£Όμ΄μΌ νλ€.
#νμ λ°κΎΈκΈ°
import numpy as np
C=np.array([1,2,3])
#νλ³ν μ
print(C.shape)
C=C.reshape(1, 3)
#νλ³ν ν
print(C.shape)
OUTPUT>>
(3,)
(1, 3)
πΎλ¨Έμ λ¬λ06κ° μπΎ