daviddoji/numpy-100
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fixed typos

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q42: array -> arrays
q48: value -> values
q53: integer -> integer array
q71: mulitply -> multiply
q80: extract -> extracts
q90: combinations -> combination
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Emmet-Ray 2025-04-09 16:21:14 +08:00 committed by GitHub
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1 changed files with 6 additions and 6 deletions
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source/exercises100.ktx
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@ -506,7 +506,7 @@ Z = np.arange(10)
np.add.reduce(Z)
< q42
Consider two random array A and B, check if they are equal (★★☆)
Consider two random arrays A and B, check if they are equal (★★☆)
< h42
hint: np.allclose, np.array_equal
@ -586,7 +586,7 @@ C = 1.0 / np.subtract.outer(X, Y)
print(np.linalg.det(C))
< q48
Print the minimum and maximum representable value for each numpy scalar type (★★☆)
Print the minimum and maximum representable values for each numpy scalar type (★★☆)
< h48
hint: np.iinfo, np.finfo, eps
@ -659,7 +659,7 @@ D = scipy.spatial.distance.cdist(Z,Z)
print(D)
< q53
How to convert a float (32 bits) array into an integer (32 bits) in place?
How to convert a float (32 bits) array into an integer (32 bits) array in place?
< h53
hint: view and [:] =
@ -969,7 +969,7 @@ Z0[::nz+1] = Z
print(Z0)
< q71
Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)
Consider an array of dimension (5,5,3), how to multiply it by an array with dimensions (5,5)? (★★★)
< h71
hint: array[:, :, None]
@ -1145,7 +1145,7 @@ def distance_points_to_lines(p: np.ndarray, p_1: np.ndarray, p_2: np.ndarray) ->
distance_points_to_lines(p, P0, P1)
< q80
Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)
Consider an arbitrary array, write a function that extracts a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)
< h80
hint: minimum maximum
@ -1366,7 +1366,7 @@ print (Z[np.argsort(Z)[-n:]])
print (Z[np.argpartition(-Z,n)[:n]])
< q90
Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)
Given an arbitrary number of vectors, build the cartesian product (every combination of every item) (★★★)
< h90
hint: np.indices