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import numpy as np
# lambda 함수
f1 = lambda x: x+100
print(f1(10)) # 110
def test_lambda(a, b):
print(a, b)
a = 100
b = 200
f2 = lambda x, y: test_lambda(a, b)
f3 = lambda x, y: test_lambda(x, y)
f2(1, 2) # 100 200
f3(1, 2) # 1 2
# list comprehension
data = [[1, 5], [2, 9], [3, 17], [4, 21]]
x_data = [x[0] for x in data]
y_data = [x[1] for x in data]
print(x_data) #[1, 2, 3, 4]
print(y_data) #[5, 9, 17, 21]
# with 구문 => with 블록을 벗어나는 순간 자동으로 close
with open("./test_file", 'w') as f:
f.write("good morning")
# iterator
data = np.array([[1, 5], [2, 9], [3, 17], [4, 21]])
it = np.nditer(data, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
idx = it.multi_index
print(data[idx], end=" ") # 1 5 2 9 3 17 4 21
it.iternext()
print()
# numpy 행렬에 row or column 추가
matrix = np.array([[1, 2], [3, 4]])
print(matrix[:, -1].shape) # (2,)
print(matrix[:, [-1]].shape) # (2, 1)
add_row = np.array([5, 6]).reshape(1, 2)
matrix = np.concatenate((matrix, add_row), axis=0)
print(matrix)
add_column = np.array([10, 20, 30]).reshape(3, 1)
matrix = np.concatenate((matrix, add_column), axis=1)
print(matrix)
# 머신러닝 모델 구현 시, numpy 유틸리티 함수
data = np.array([1, 2, 3, 4])
print(np.sum(data))
print(np.exp(data))
print(np.log(data))
print(np.max(data))
print(np.min(data))
print(np.argmax(data)) # 3
print(np.argmin(data)) # 0
# 수치미분 구현 (1)
def test_func(x):
return x**2
def numerical_derivative(func, x):
delta = 1e-4
return (func(x+delta)-func(x-delta))/(2*delta)
print(numerical_derivative(test_func, 5)) # 9.9999 == 10
# 수치미분 구현 (2)
def test_func2(x):
v1 = x[0]
v2 = x[1]
return v1**2 + v2*3
def numerical_derivative_2(func, x):
delta = 1e-4
grad = np.zeros_like(x)
it2= np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
while not it2.finished:
idx = it2.multi_index
temp = x[idx]
x[idx] = float(temp) + delta
f1 = func(x)
x[idx] = float(temp) - delta
f2 = func(x)
grad[idx] = (f1-f2)/(2*delta)
x[idx] = temp
it2.iternext()
return grad
print(numerical_derivative_2(test_func2, np.array([2.0, 3.0]))) # [4. 3.]
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