品牌服装网站建设现状,源代码查看WordPress文件,运营方案,英文商城网站文章目录 相关文献测试电脑配置展开元素是list的list在numpy数组上映射函数的最有效方法数组numpy中唯一值的最有效频率计数方法反转numpy数组的最有效方法如何向 numpy 数组添加额外的列将 numpy 矩阵初始化为零或一以外的值 作者#xff1a;小猪快跑 基础数学计算数学小猪快跑 基础数学计算数学从事优化领域5年主要研究方向MIP求解器、整数规划、随机规划、智能优化算法
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测试电脑配置 博主三千元电脑的渣渣配置 CPU model: AMD Ryzen 7 7840HS w/ Radeon 780M Graphics, instruction set [SSE2|AVX|AVX2|AVX512] Thread count: 8 physical cores, 16 logical processors, using up to 16 threads 展开元素是list的list
输入
[[1, 2, 3],[4, 5, 6],[7],[8, 9]
]输出
[1, 2, 3, 4, 5, 6, 7, 8, 9]我们从两个维度测试一种是sub-list的元素数量固定增加list里面的元素
f lambda n: [list(range(10))] * n
f(1) [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
f(3) [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]另一种是list里面的元素数量固定增加sub-list的元素数量
g lambda n: [list(range(n))] * 10
g(1) [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]
g(3) [[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]]import functools
import itertools
import operator
import numpy as np
import perfplot
from matplotlib import pyplot as pltdef forfor(a):return [item for sublist in a for item in sublist]def sum_brackets(a):return sum(a, [])def functools_reduce(a):return functools.reduce(operator.concat, a)def functools_reduce_iconcat(a):return functools.reduce(operator.iconcat, a, [])def itertools_chain(a):return list(itertools.chain.from_iterable(a))def numpy_flat(a):return list(np.array(a).flat)def numpy_concatenate(a):return list(np.concatenate(a))def extend(a):out []for sublist in a:out.extend(sublist)return outb perfplot.bench(setuplambda n: [list(range(10))] * n,# setuplambda n: [list(range(n))] * 10,kernels[forfor,sum_brackets,functools_reduce,functools_reduce_iconcat,itertools_chain,numpy_flat,numpy_concatenate,extend,],n_range[2 ** k for k in range(16)],xlabelnum lists (of length 10),# xlabellen lists (10 lists total)
)plt.figure(dpi300)
b.save(out.png)
b.show()在numpy数组上映射函数的最有效方法
我们有numpy数组期望对每个元素做同样的函数操作
import numpy as np x np.array([1, 2, 3, 4, 5])# Obtain array of square of each element in x
squarer lambda t: t ** 2
squares np.array([squarer(xi) for xi in x])如果你试图向量化的函数已经被向量化了就像上面的x**2例子那么使用它比其他任何东西都快得多注意对数比例 import numpy as np
import perfplot
import mathfrom matplotlib import pyplot as pltdef f(x):# return math.sqrt(x)return np.sqrt(x)vf np.vectorize(f)def array_for(x):return np.array([f(xi) for xi in x])def array_map(x):return np.array(list(map(f, x)))def fromiter(x):return np.fromiter((f(xi) for xi in x), x.dtype)def vectorize(x):return np.vectorize(f)(x)def vectorize_without_init(x):return vf(x)b perfplot.bench(setupnp.random.rand,n_range[2 ** k for k in range(20)],kernels[f,array_for,array_map,fromiter,vectorize,vectorize_without_init,],xlabellen(x),
)plt.figure(dpi300)
b.save(out1.png)
b.show()数组numpy中唯一值的最有效频率计数方法
输入
[14, 31, 27, 27, 31, 13, 14, 13]输出
Item frequency:
[[13 2][14 2][27 2][31 2]
]# numpy most efficient frequency counts for unique values in an array
import numpy as np
import pandas as pd
import perfplot
from matplotlib import pyplot as pltdef bincount(a):y np.bincount(a)ii np.nonzero(y)[0]return np.vstack((ii, y[ii])).Tdef unique(a):unique, counts np.unique(a, return_countsTrue)return np.asarray((unique, counts)).Tdef unique_count(a):unique, inverse np.unique(a, return_inverseTrue)count np.zeros(len(unique), dtypeint)np.add.at(count, inverse, 1)return np.vstack((unique, count)).Tdef pandas_value_counts(a):out pd.value_counts(pd.Series(a))out.sort_index(inplaceTrue)out np.stack([out.keys().values, out.values]).Treturn outb perfplot.bench(setuplambda n: np.random.randint(0, 1000, n),kernels[bincount, unique, unique_count, pandas_value_counts],n_range[2 ** k for k in range(26)],xlabellen(a),
)
plt.figure(dpi300)
b.save(out.png)
b.show()反转numpy数组的最有效方法
输入
[1, 3, 2]输出
[2, 3, 1]import numpy
import perfplot
from matplotlib import pyplot as pltb perfplot.bench(setuplambda n: numpy.random.randint(0, 1000, n),kernels[lambda a: a[::-1],lambda a: numpy.ascontiguousarray(a[::-1]),lambda a: numpy.fliplr([a])[0],],labels[a[::-1], ascontiguousarray(a[::-1]), fliplr],n_range[2 ** k for k in range(25)],xlabellen(a),
)
plt.figure(dpi300)
b.save(out.png)
b.show()如何向 numpy 数组添加额外的列
输入
[14, 31, 27, 27]输出
[[14, 14],[31, 31],[27, 27],[27, 27]
]import numpy as np
import perfplot
from matplotlib import pyplot as pltb perfplot.bench(setupnp.random.rand,kernels[lambda a: np.c_[a, a],lambda a: np.ascontiguousarray(np.stack([a, a]).T),lambda a: np.ascontiguousarray(np.vstack([a, a]).T),lambda a: np.column_stack([a, a]),lambda a: np.concatenate([a[:, None], a[:, None]], axis1),lambda a: np.ascontiguousarray(np.concatenate([a[None], a[None]], axis0).T),lambda a: np.stack([a, a]).T,lambda a: np.vstack([a, a]).T,lambda a: np.concatenate([a[None], a[None]], axis0).T,],labels[c_,ascont(stack),ascont(vstack),column_stack,concat,ascont(concat),stack (non-cont),vstack (non-cont),concat (non-cont),],n_range[2 ** k for k in range(23)],xlabellen(a),
)
plt.figure(dpi300)
b.save(out.png)
b.show()将 numpy 矩阵初始化为零或一以外的值 import numpy
import perfplot
from matplotlib import pyplot as pltval 42.0def fill(n):a numpy.empty(n)a.fill(val)return adef colon(n):a numpy.empty(n)a[:] valreturn adef full(n):return numpy.full(n, val)def ones_times(n):return val * numpy.ones(n)def list(n):return numpy.array(n * [val])b perfplot.bench(setuplambda n: n,kernels[fill, colon, full, ones_times, list],n_range[2 ** k for k in range(20)],xlabellen(a),
)
plt.figure(dpi300)
b.save(out.png)
b.show()
