numpyの理解を深めるために100問エクササイズに挑戦してみました。
今回は61問目から70問目までです。
61. Find the nearest value from a given value in an array.
Z = np.random.uniform(0,1,10)
print(Z)
z = 0.5
print(np.abs(Z - z).argmin())
m = Z.flat[np.abs(Z - z).argmin()]
print(m)[0.27576656 0.4030415 0.52931972 0.09513933 0.54607919 0.81908521 0.96007619 0.2462154 0.11258337 0.03125006]
2
0.529319720042370862. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator?
A = np.arange(3).reshape(3,1)
print(A)
B = np.arange(3).reshape(1,3)
print(B)
it = np.nditer([A,B,None])
for x,y,z in it: z[...] = x + y
print(it.operands[2])[[0]
[1]
[2]]
[[0 1 2]]
[[0 1 2]
[1 2 3]
[2 3 4]]63. Create an array class that has a name attribute.
class NamedArray(np.ndarray):
def __new__(cls, array, name="no name"):
obj = np.asarray(array).view(cls)
obj.name = name
return obj
def __array_finalize__(self, obj):
if obj is None: return
self.info = getattr(obj, 'name', "no name")
Z = NamedArray(np.arange(10), "range_10")
print(Z.name)range_1064. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)?
Z = np.ones(10)
print(Z)
I = np.random.randint(0,len(Z),20)
print(I)
Z += np.bincount(I, minlength=len(Z))
print(Z)[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[0 2 2 7 4 0 9 9 4 1 5 8 1 1 0 2 5 9 9 6]
[4. 4. 4. 1. 3. 3. 2. 2. 2. 5.]65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)?
X = [1,2,3,4,5,6]
I = [1,3,9,3,4,1]
F = np.bincount(I,X)
print(F)[0. 7. 0. 6. 5. 0. 0. 0. 0. 3.]66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors.
w, h = 256, 256
I = np.random.randint(0, 4, (h, w, 3)).astype(np.ubyte)
colors = np.unique(I.reshape(-1, 3), axis=0)
n = len(colors)
print(n)6467. Considering a four dimensions array, how to get sum over the last two axis at once?
A = np.random.randint(0,10,(3,4,3,4))
# solution by passing a tuple of axes (introduced in numpy 1.7.0)
sum = A.sum(axis=(-2,-1))
print(sum)
# solution by flattening the last two dimensions into one
# (useful for functions that don't accept tuples for axis argument)
sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)
print(sum)[[52 65 59 52]
[37 44 78 65]
[47 42 46 40]]
[[52 65 59 52]
[37 44 78 65]
[47 42 46 40]]68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices?
D = np.random.uniform(0,1,100)
S = np.random.randint(0,10,100)
D_sums = np.bincount(S, weights=D)
D_counts = np.bincount(S)
D_means = D_sums / D_counts
print(D_means)[0.59106947 0.48393144 0.39774691 0.55092921 0.54903803 0.40256009 0.54447985 0.31561391 0.43108683 0.5625863 ]69. How to get the diagonal of a dot product?
A = np.random.uniform(0,1,(5,5))
B = np.random.uniform(0,1,(5,5))
# Slow version
print(np.diag(np.dot(A, B)))
# Fast version
print(np.sum(A * B.T, axis=1))
# Faster version
print(np.einsum("ij,ji->i", A, B))[2.43058829 1.88526771 1.01422527 1.45964625 1.12584508]
[2.43058829 1.88526771 1.01422527 1.45964625 1.12584508]
[2.43058829 1.88526771 1.01422527 1.45964625 1.12584508]70. Consider the vector [1, 2, 3, 4, 5], how to build a new vector with 3 consecutive zeros interleaved between each value?
Z = np.array([1,2,3,4,5])
nz = 3
Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))
Z0[::nz+1] = Z
print(Z0)[1. 0. 0. 0. 2. 0. 0. 0. 3. 0. 0. 0. 4. 0. 0. 0. 5.]次回は71〜80問目に挑戦してみます。