如果我已经读取了相同的数组,我希望遍历数组,然后跳到下一个数组。下面的代码可以工作,但我正在搜索一个更“pythonic”风格的解决方案。
from sklearn import datasets
import numpy as np
iris = datasets.load_iris()
X = iris.data[:, :2]
read = []
for x in X:
temp = True
for r in read:
if np.array_equal(x, r):
temp = False
if temp:
现在,我正在尝试理解索引numpy结构化数组的可能方法,而且我有点被它困住了。仅举几个简单的例子:
import numpy as np
arr = np.array(zip(range(5), range(5, 10)), dtype=[('a', int), ('b', int)])
arr[0] # first row (record)
arr[(0,)] # the same, as expected
arr['a'] # field 'a' of each record
arr[('a',)] # &
我想构造一个继承自numpy.ndarray的类,这样它就可以像numpy数组(+,-,*,/,...)一样执行正常的操作。我想要更改的唯一一件事是我们访问数据中项的方式。例如:
import numpy as np
from PIL import Image
class Data(np.ndarray):
"""
Something magical here
"""
img = np.asarray(Image.open('lena.jpg'))
data = img.view(Data)
data[
我想写一个pandas.core.index.Index的子类。我正在遵循可以在中找到的ndarray子类化指南。下面是我的代码:
import numpy as np
import pandas as pd
class InfoIndex(pd.core.index.Index):
def __new__(subtype, data, info=None):
# Create the ndarray instance of our type, given the usual
# ndarray input arguments. This will
num_samples = 10
def predict(x):
sampled_models = [guide(None, None) for _ in range(num_samples)]
yhats = [model(x).data for model in sampled_models]
mean = torch.mean(torch.stack(yhats), 0)
return np.argmax(mean.numpy(), axis=1)
print('Prediction when network is forced to predi
我正在尝试对从OpenCV (2.3.1) Python绑定生成的两个numpy数组进行通道比较。因此,我有一个形状为(x,y)的掩模(数组/图像/通道),我想将其与形状为(x,y,3)的RGB数组/图像的每个通道进行比较。
考虑到numpy的切片,我可以更接近我想要的东西:
channel = ndarr[...,i:i+1] #where i is the channel I want
..。但这会返回一个形状为(x,y,1)的ndarray,而不是我需要的(x,y)。有没有一种优雅的方法可以在单个切片操作中做到这一点。如果做不到,那么最简单的方法是什么呢?
如何按嵌套的dtype对numpy数组进行排序?
我希望通过数组中的第一个元素对numpy数组进行排序
import numpy as np
from random import randint
# create dummy data
test = np.array([[[randint(1, 10) for _ in range(3)]] for _ in range(10)])
dtype = [('response', [('x', 'f'),('y', 'f'),('x', 'f
我想在三维numpy数组上的for循环中释放GIL
cdef np.ndarray[DTYPE_t,ndim=3] array=np.ones((10000000,4,2))
cdef np.ndarray[DTYPE_t,ndim=2] sliced_array
cdef int i
cdef int N=array.shape[0]
for i in range(N):
sliced_array=array[i]
#perform computations on slice
当我查看由Cython生成的html时,它看起来像是在调用Python,而它正在执行sliced_array
numpy版本1.20.1
的法律博客,我需要更多的细节,这篇文章有很多代码。
def quadratic_formula(a, b, c):
"""just like the song
Args:
a (numpy.ndarray): shape(N,1)
b (numpy.ndarray): shape(N,1)
c (numpy.ndarray): shape(N,1)
Returns:
numpy.ndarray: shape(N,2)
* [soln_a