I would like to have the norm of one NumPy array. More specifically, I am looking for an equivalent version of this function
我想拥有一个NumPy数组的规范。更具体地说,我正在寻找此功能的等效版本
def normalize(v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / norm
Is there something like that in skearn
or numpy
?
在卷须或笨蛋中有类似的东西吗?
This function works in a situation where v
is the 0 vector.
此函数适用于v为0向量的情况。
5 个解决方案
#1
86
If you're using scikit-learn you can use sklearn.preprocessing.normalize
:
如果你正在使用scikit-learn,你可以使用sklearn.preprocessing.normalize:
import numpy as np
from sklearn.preprocessing import normalize
x = np.random.rand(1000)*10
norm1 = x / np.linalg.norm(x)
norm2 = normalize(x[:,np.newaxis], axis=0).ravel()
print np.all(norm1 == norm2)
# True
#2
28
I would agree that it were nice if such a function was part of the included batteries. But it isn't, as far as I know. Here is a version for arbitrary axes, and giving optimal performance.
我同意如果这样的功能是包含电池的一部分,那就太好了。但据我所知,它并非如此。这是任意轴的版本,并提供最佳性能。
import numpy as np
def normalized(a, axis=-1, order=2):
l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
l2[l2==0] = 1
return a / np.expand_dims(l2, axis)
A = np.random.randn(3,3,3)
print(normalized(A,0))
print(normalized(A,1))
print(normalized(A,2))
print(normalized(np.arange(3)[:,None]))
print(normalized(np.arange(3)))
#3
11
You can specify ord to get the L1 norm. To avoid zero division I use eps, but that's maybe not great.
您可以指定ord来获得L1规范。为了避免零分割,我使用eps,但这可能不是很好。
def normalize(v):
norm=np.linalg.norm(v, ord=1)
if norm==0:
norm=np.finfo(v.dtype).eps
return v/norm
#4
2
If you have multidimensional data and want each axis normalized to itself:
如果您有多维数据并希望每个轴都标准化为自身:
def normalize(d):
# d is a (n x dimension) np array
d -= np.min(d, axis=0)
d /= np.ptp(d, axis=0)
return d
Uses numpys peak to peak function.
使用numpys峰峰功能。
#5
0
There is also the function unit_vector()
to normalize vectors in the popular transformations module by Christoph Gohlke:
在Christop Gohlke的流行转换模块中还有一个函数unit_vector()来规范化向量:
import transformations as trafo
import numpy as np
data = np.array([[1.0, 1.0, 0.0],
[1.0, 1.0, 1.0],
[1.0, 2.0, 3.0]])
print(trafo.unit_vector(data, axis=1))
#1
86
If you're using scikit-learn you can use sklearn.preprocessing.normalize
:
如果你正在使用scikit-learn,你可以使用sklearn.preprocessing.normalize:
import numpy as np
from sklearn.preprocessing import normalize
x = np.random.rand(1000)*10
norm1 = x / np.linalg.norm(x)
norm2 = normalize(x[:,np.newaxis], axis=0).ravel()
print np.all(norm1 == norm2)
# True
#2
28
I would agree that it were nice if such a function was part of the included batteries. But it isn't, as far as I know. Here is a version for arbitrary axes, and giving optimal performance.
我同意如果这样的功能是包含电池的一部分,那就太好了。但据我所知,它并非如此。这是任意轴的版本,并提供最佳性能。
import numpy as np
def normalized(a, axis=-1, order=2):
l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
l2[l2==0] = 1
return a / np.expand_dims(l2, axis)
A = np.random.randn(3,3,3)
print(normalized(A,0))
print(normalized(A,1))
print(normalized(A,2))
print(normalized(np.arange(3)[:,None]))
print(normalized(np.arange(3)))
#3
11
You can specify ord to get the L1 norm. To avoid zero division I use eps, but that's maybe not great.
您可以指定ord来获得L1规范。为了避免零分割,我使用eps,但这可能不是很好。
def normalize(v):
norm=np.linalg.norm(v, ord=1)
if norm==0:
norm=np.finfo(v.dtype).eps
return v/norm
#4
2
If you have multidimensional data and want each axis normalized to itself:
如果您有多维数据并希望每个轴都标准化为自身:
def normalize(d):
# d is a (n x dimension) np array
d -= np.min(d, axis=0)
d /= np.ptp(d, axis=0)
return d
Uses numpys peak to peak function.
使用numpys峰峰功能。
#5
0
There is also the function unit_vector()
to normalize vectors in the popular transformations module by Christoph Gohlke:
在Christop Gohlke的流行转换模块中还有一个函数unit_vector()来规范化向量:
import transformations as trafo
import numpy as np
data = np.array([[1.0, 1.0, 0.0],
[1.0, 1.0, 1.0],
[1.0, 2.0, 3.0]])
print(trafo.unit_vector(data, axis=1))