how to normalize a 1d numpy array
# Foe 1d array
an_array = np.array([0.1,0.2,0.3,0.4,0.5])
norm = np.linalg.norm(an_array)
normal_array = an_array/norm
print(normal_array)
#[0.2,0.4,0.6,0.8,1] (Should be, I didin't run the code)how to normalize a 1d numpy array
# Foe 1d array
an_array = np.array([0.1,0.2,0.3,0.4,0.5])
norm = np.linalg.norm(an_array)
normal_array = an_array/norm
print(normal_array)
#[0.2,0.4,0.6,0.8,1] (Should be, I didin't run the code)javascript array flatten
// flat(depth),
// depth is optional: how deep a nested array structure
// should be flattened.
// default value of depth is 1
const arr1 = [1, 2, [3, 4]];
arr1.flat();
// [1, 2, 3, 4]
const arr2 = [1, 2, [3, 4, [5, 6]]];
arr2.flat();
// [1, 2, 3, 4, [5, 6]]
const arr3 = [1, 2, [3, 4, [5, 6]]];
arr3.flat(2);
// [1, 2, 3, 4, 5, 6]
const arr4 = [1, 2, [3, 4, [5, 6, [7, 8, [9, 10]]]]];
arr4.flat(Infinity);
// [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]numpy flatten
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])flatten image python numpy
x_data = np.array( [np.array(cv2.imread(imagePath[i])) for i in range(len(imagePath))] )
pixels = x_data.flatten().reshape(1000, 12288)
print pixels.shapeflat numpy array
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])Copyright © 2021 Codeinu
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