https://github.com/rougier/numpy-100/blob/master/100%20Numpy%20exercises.md
51.新建一个向量表示(x,y)和(r,g,b)
z = np.zeros(
10,
[(
'position', [(
'x',
float,
1),(
'y',
float,
1)]),
(
'color', [(
'r',
float,
1), (
'g',
float,
1), (
'b',
float,
1)])])
52.在一个10*2的矩阵求出两点间距离
z = np
.random.random((
10,
2))
# atleast_2d查看至少2维的数组
x,
y = np
.atleast_2d(
z[:,
0]), np
.atleast_2d(
z[:,
1])
D = np
.sqrt((
x-
x.T)**
2 + (
y-
y.T)**
2)
#更快的版本
import scipy
import scipy
.spatial
z = np
.random.random((
10,
2))
D = scipy
.spatial.distance.cdist(
z,
z)
53.怎样原地把float的数组转换成int
z = np
.arange(
10, dtype=np
.float32)
z =
z.astype(np
.int.32, copy=False)
54.怎样从文件读取(有问题)
from io
import StringIO
s = StringIO(
"""1,2,3,4,5\n
6, , ,7,8\n
, ,9,10,11\n""")
z = np.genfromtxt(s, delimiter=
",", dtype=np.int)
55.和numpy枚举相同的做法
z = np.arange(
9).reshape(
3,
3)
for index, value = in np.ndenumerate(z) :
print(
index, value)
for index in np.ndindex(z.shape) :
print(
index, z[
index])
56.生成一个2维高斯分布
x,
y = np
.meshgrid(np
.linspace(-
1,
1,
10), np
.linspace(-
1,
1,
10))
D = np
.sqrt(
x**
x +
y**
y)
sigma, mu =
1.0,
0.0
G = np
.exp(-((D-mu)**
2 / (
2.0 *sigma**
2)))
57.怎样在2维数组随机替换p个元素
n =
10
p =
3
z = np
.zeros((n, n))
np
.put(
z, np
.random.choice(range(n*n), p, replace=False),
1)
58.每行减平均数
y = x - x.mean(axis=1, keepdimas=True)
59.以第n列排序为重新排序矩阵
z = np
.random.randint(
0,
10,(
3,
3))
z[
z[:,
1]
.argsort()]
60.怎样发现给定的二维矩阵有空列
z = np
.random.randint(
0,
3,(
3,
10))
print(~
z.any(axis=
0)
.any())
61.给定数组里的值找到最近的值
z = np
.random.uniform(
0,
1,
10)
x =
0.5
m =
z.flat[np
.abs(
z-
x)
.argmin()]
62.如何用生成器计算(1,3)和(3,1)数组的和
a = np.arange(
3).reshape(
3,
1)
b = np.arange(
3).reshape(
1,
3)
it = np.nditer([a, b, None])
for x, y, z
in it:
z[
...] = x + y
print(it.operands[
2])
63.新建一个数组类有数组属性
class NameArray(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 = NameArray(np.arange(
10),
"range_10")
print(z.name)
64.新建一个向量,如何用第二个向量做下标向第一个元素加一
z = np
.ones(
10)
I = np
.random.randint(
0, len(
z),
20)
z += np
.bincount(I, minlength=len(
z))
#bincount 返回每个数字出现次数
65.根据I,累计X到F
X = [
1,
2,
3,
4,
5,
6]
I = [
1,
3,
9,
3,
4,
1]
F = np.bincount(
I, X) #相当于把X当权重付给
I,然后计数
66.给定一个(w,h,3)的图片,计算不一样的颜色个数
w, h =
16,
16
I = np.random,randint(
0,
2,(h,w,
3)).astype(np.ubyte)
F = I[
...,
0]*
256*
256 + I[
...,
1]*
256 + I[
...,
2]
n = len(np.unique(
F))
67.给定一个四维的数组,如果一次性求最后两维的总和
a = np.
random.randint(
0,
10,(
3,
4,
3,
4))
sum =
a.reshape(
a.shape[:-
2] + (-
1,)).
sum(axis=-
1)
68.给定一个一维向量D,怎样用一个一样大小的向量S描述子集的索引计算D子集的平均值
例如 D=[0.1, 0.2, 0.3] S = [0, 1, 0] 即索引值为0的平均值为 (0.1+0.3)/2=0.2
D = np
.random.uniform(
0,
1,
100)
S = np
.random.randint(
0,
10,
100)
D_sums = np
.bincount(S, weight=D)
D_count = np
.bincount(S)
D_means = D_sums / D_counts
69.怎样获得矩阵乘法结果的对角线
a = np
.random.uniform(
0,
1,(
5,
5))
b = np
.random.uniform(
0,
1,(
5,
5))
#慢版本
np
.diag(np
.dot(a, b))
#快版本
np
.sum(a * b
.T, axis=
1)
#更快版本,einsum貌似可以根据字符串来描述加的方法
np
.einsum(
"ij, ji->i", a, b)
70.给定一个[1,2,3,4],怎样新建一个新的向量两元素间有连续3个零
z = np.
array([
1,
2,
3,
4,
5])
nz =
3
z0 = np.zeros(
len(z) + (
len(z)-
1)*(nz))
z0[::nz+
1] = z
71.给定一个(5,5,3)的数组,怎样和一个(5,5)相乘
a = np.ones((
5,
5,
3))
b =
2*np.ones((
5,
5))
a*b[:,:,None]
72.怎样交换两行
a = np.arange(
25).reshape(
5,
5)
#
1赋值
0,
0赋值
1。[
0,
1]代表两行
a
[[0,1]] = a
[[1,0]]
73.给定10个3元组来表述10个三角形,线段两两组合
faces = np.random.randint(
0,
100,(
10,
3))
F = np.roll(faces.repeat(
2, axis=
1), -
1, axis=-
1)
F = F.reshape(len(
F)*
3,
2)
F = np.sort(
F, axis=
1)
G = F.view(dtype=[(
'p0', F.dtype), (
'p1', F.dtype)])
G = np.unique(G)
74.给定一个bincount数组C,怎样生成一个A,np.bincount == C
c = np
.bincount([
1,
1,
2,
3,
4,
4,
6])
[
0,
1,
2,
3,
4,
5,
6]
0重复c[
0]次
1重复c[
1]次
a = np
.repeat(np
.arange(len(c)), c)
75.用计算滑窗内的平均值
def moving_average(a, n=3) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n-
1:] / n
z = np.arange(
20)
moving_average(z, n=
3)
76.给定一个一维向量,构建一个新二维向量,第一行z[0] z[1] z[2],后面每行往后移一位
from numpy.lib
import stride_tricks
def rolling(a, window) :
shape = (a.size - window +
1, window)
strides = (a.itemsize, a.itemsize)
return stride_tricks.as_strided(a, shape=shapem strides=strides)
z = rolling(np.arange(
10),
3)
77.布尔去反,或原地改浮点数符号
z = np
.random.randint(
0,
2,
100)
np
.logical_not(
z,
out=
z)
z = np
.random.uniform(-
1.0,
1.0,
100)
np
.negative(
z,
out=
z)
78.给定两个点集合P0, P1描述线和一个点p,怎样比较p点到每条线的距离
def distance(P0, P1, p) :
T = P1 - P0
L = (
T**
2).sum(axis=
1)
U = -((P0[:,
0]-p[
...,
0]) *
T[:,
0] + (P0[:,
1]-p[
...,
1]) *
T[:,
1]) / L
U = U.reshape(len(U),
1)
D = P0 + U*
T - p
return np.sqrt((D**
2).sum(axis=
1))
P0 = np.random.uniform(-
10,
10,(
10,
2))
P1 = np.random.uniform(-
10,
10,(
10,
2))
p = np.random.uniform(-
10,
10,(
1,
2))
ditance(P0,P1,p)
79.每个点到每条线的距离
P0 = np
.random.uniform(-
10,
10,(
10,
2))
P1 = np
.random.uniform(-
10,
10(
10,
2))
p = np
.random.uniform(-
10,
10,(
10,
2))
np
.array([distance(P0,P1,p_i) for p_i
in p])
80.给定一个随机矩阵,写一个函数提取一个子集合,开始点位给定位置,其余用fill元素填充
Z = np
.random.randint(
0,
10,(
10,
10))
shape = (
5,
5)
fill =
0
position = (
1,
1)
R = np
.ones(shape, dtype=
Z.dtype)*fill
P = np
.array(list(position))
.astype(int)
Rs = np
.array(list(R
.shape))
.astype(int)
Zs = np
.array(list(
Z.shape))
.astype(int)
R_start = np
.zeros((len(shape),))
.astype(int)
R_stop = np
.array(list(shape))
.astype(int)
Z_start = (P-Rs//
2)
Z_stop = (P+Rs//
2)+Rs%
2
R_start = (R_start - np
.minimum(Z_start,
0))
.tolist()
Z_start = (np
.maximum(Z_start,
0))
.tolist()
R_stop = np
.maximum(R_start, (R_stop - np
.maximum(Z_stop-Zs,
0)))
.tolist()
Z_stop = (np
.minimum(Z_stop,Zs))
.tolist()
r = [slice(start,stop) for start,stop
in zip(R_start,R_stop)]
z = [slice(start,stop) for start,stop
in zip(Z_start,Z_stop)]
R[r] =
Z[
z]
print(
Z)
print(R)
81.给定一个z=[1:15],怎样生成z[0] z[1] z[2] z[3],每行左移1的新矩阵
z = np
.arange(
1,
15,dtype=np
.unit32)
r = stride_tricks
.as_strided(
z, (
11,
4), (
4,
4))
82.计算矩阵的秩
z = np
.random.uniform(
0,
1,(
10,
10))
# 分解矩阵z = u * np.diag(s) * v
u, s, v = np
.linalg.svd(
z)
# 大于0
rank = np
.sum(s >
1e-10)
83.找众数
z = np
.random.randint(
0,
10,
50)
np
.bincount(
z)
.argmax()
84.在10*10的矩阵内用3*3的滑窗新建一个向量
z = np
.random.randint(
0,
5,(
10,
10))
n =
3
i =
1+(
z.shape[
0]-
3)
j =
1+(
z.shape[
1]-
3)
c = stride_tricks
.as_strided(
z, shape(i,j,n,n), strides=
z.strides+
z.strides)
85.新建一个2维数组,z[i,j]=z[j,i],修改一个值能自动更改
class Symetric(np.ndarray):
def __setitem__(self, index, value):
i,j = index
super(Symetric, self).__setitem__((i,j), value)
super(Symetric, self).__setitem__((j,i), value)
def symetric(Z):
return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)
S = symetric(np.random.randint(
0,
10,(
5,
5)))
S[
2,
3] =
42
86.假设p个(n,n)的矩阵,p个(n,1)的向量,怎样一次性计算和
p, n =
10,
20
M = np.ones((p,n,n))
V = np.ones((p,n,
1))
#
0跟
0,
2跟
1相乘
S = np.tensordot(M, V, axes=
[[0,2], [0,1]])
87.给定一个16*16的数组,怎样获得模块和(模块大小4*4)
z = np
.ones((
16,
16))
k =
4
s = np
.reduceat(np
.add.reduceat(
z, np
.arange(
0,
z.shape[
0], k),axis=
0),np
.arange(
0,
z.shape[
1],k), axis=
1)
88. Game of Life
def iterate(
Z)
:
N = (
Z[
0:-2,
0:-2] +
Z[
0:-2,
1:-1] +
Z[
0:-2,
2:] +
Z[
1:-1,
0:-2] +
Z[
1:-1,
2:] +
Z[
2: ,
0:-2] +
Z[
2: ,
1:-1] +
Z[
2: ,
2:])
birth = (
N==
3) & (
Z[
1:-1,
1:-1]==
0)
survive = ((
N==
2) | (
N==
3)) & (
Z[
1:-1,
1:-1]==
1)
Z[...] =
0
Z[
1:-1,
1:-1][birth | survive] =
1
return Z
Z = np.random.randint(
0,
2,(
50,
50))
for i
in range(
100)
: Z = iterate(
Z)
print(
Z)
89. N大数
z = np
.arange(
10000)
np
.random.shuffle(
z)
n =
5
#slow
z[np
.argsort(
z)[-n:]]
#fast argpartition 小于n的放在前面,大于的放在后面
z[np
.argpartion(-
z,n)[:n]]
90.给定一个随机向量,新建一个笛卡尔组合
def cartesian(arrays):
arrays = [np.asarray(a)
for a
in arrays]
shape = (len(x)
for x
in arrays)
ix = np.indices(shape, dtype=int)
ix = ix.reshape(len(arrays), -
1).T
for n, arr
in enumerate(arrays):
ix[:, n] = arrays[n][ix[:, n]]
return ix
print (cartesian(([
1,
2,
3], [
4,
5], [
6,
7])))
91.怎样从一个数组新建一个记录数组
z = np.
array([(
"Hello" ,
2.5,
3),(
"World",
3.6,
2)])
r = np.core.records.fromarray(z.T, names=
'col1, col2, col3', formats=
'S8,f8,i8')
92.给定一个很大的z,怎样求立方
x = np.random.
rand(
5e7)
%timeit np.power(
x,
3)
%timeit x*x*x
%time np.einsum(
'i,i,i->i',
x,
x,
x)
93.给定A(8,3) B(2,2), 怎样找到A的行包括B每一行的元素不管顺序
A = np.random.randint(
0,
5,(
8,
3))
B = np.random.randint(
0,
5,(
2,
2))
C = (A[
..., np.newaxis, np.newaxis] == B)
rows = (C.sum(axis=(
1,
2,
3)) >= B.shape[
1]).nonzero()[
0]
print(rows)
94.给定一个10*3的矩阵,提取有不同值得行
z = np
.random.randint(
0,
5,(
10,
3))
e = np
.logical_and
.reduce(
z[:,
1:]==
z[:,:,-
1], axis=-
1)
u =
z[~e]
95.转换一个整数的向量,变成二进制表达的矩阵
I = np
.array([
0,
1,
2,
3,
15,
16,
32,
64,
128])
B = ((I
.reshape(-
1,
1) & (
2**np
.arange(
8))) !=
0)
.astype(int)
print(B[:,::-
1])
I = np
.array([
0,
1,
2,
3,
15,
16,
32,
64,
128], dtype=np
.uint8)
print(np
.unpackbits(I[:, np
.newaxis], axis=
1))
96.给定一个2维数组,怎样提出唯一的行
Z = np
.random.randint(
0,
2,(
6,
3))
T = np
.ascontiguousarray(
Z)
.view(np
.dtype((np
.void,
Z.dtype.itemsize *
Z.shape[
1])))
_, idx = np
.unique(T, return_index=True)
uZ =
Z[idx]
print(uZ)
97.给定两个A,B, 用einsum写出inner outer sum mul功能
a = np.
random.uniform(
0,
1,
10)
b = np.
random.uniform(
0,
1,
10)
np.einsum(
'i->',A)
np.enisum(
'i,i->i',
a,b)
np.enisum(
'i,i',
a,b)
np.einsum(
'i,j',
a,b)
98.给定一个用(x,y)描述的路,怎样等距抽样
phi = np
.arange(
0,
10*np
.pi,
0.1)
a =
1
x = a*phi*np
.cos(phi)
y = a*phi*np
.sin(phi)
dr = (np
.diff(
x)**
2 + np
.diff(
y)**
2)**
.5 # segment lengths
r = np
.zeros_like(
x)
r[
1:] = np
.cumsum(dr)
# integrate path
r_int = np
.linspace(
0, r
.max(),
200)
# regular spaced path
x_int = np
.interp(r_int, r,
x)
# integrate path
y_int = np
.interp(r_int, r,
y)
99.给定一个整数n和二维数组X,从x中选出符合n分布的行
X = np.asarray(
[[1.0, 0.0, 3.0, 8.0],
[2.0, 0.0, 1.0, 1.0],
[1.5, 2.5, 1.0, 0.0]])
n =
4
M = np.logical_and.reduce(np.mod(X,
1) ==
0, axis=-
1)
M &= (X.sum(axis=-
1) == n)
print(X[M])
100.重新抽样,计算每个样本的平均值,计算平均值的百分位数的值
X = np
.random.randn(
100)
# random 1D array
N =
1000 # number of bootstrap samples
idx = np
.random.randint(
0,
X.size, (N,
X.size))
means =
X[idx]
.mean(axis=
1)
confint = np
.percentile(means, [
2.5,
97.5])
print(confint)