Cara menggunakan shuffle index python

I have two numpy arrays of different shapes, but with the same length (leading dimension). I want to shuffle each of them, such that corresponding elements continue to correspond -- i.e. shuffle them in unison with respect to their leading indices.,This solution could be adapted to the case that a and b have different dtypes.,This works...but it's a little scary, as I see little guarantee it'll continue to work -- it doesn't look like the sort of thing that's guaranteed to survive across numpy version, for example.,If you don't like this, a different solution would be to store your data in one array instead of two right from the beginning, and create two views into this single array simulating the two arrays you have now. You can use the single array for shuffling and the views for all other purposes.

Table of Contents

• Answer #3:
• How do you shuffle two arrays in Python?
• How do you shuffle two arrays together?
• Can you shuffle an array in Python?
• How do you randomize a NumPy array in Python?

This code works, and illustrates my goals:

def shuffle_in_unison(a, b):
   assert len(a) == len(b)
shuffled_a = numpy.empty(a.shape, dtype = a.dtype)
shuffled_b = numpy.empty(b.shape, dtype = b.dtype)
permutation = numpy.random.permutation(len(a))
for old_index, new_index in enumerate(permutation):
   shuffled_a[new_index] = a[old_index]
shuffled_b[new_index] = b[old_index]
return shuffled_a, shuffled_b

For example:

>>> a = numpy.asarray([
      [1, 1],
      [2, 2],
      [3, 3]
   ]) >>>
   b = numpy.asarray([1, 2, 3]) >>>
   shuffle_in_unison(a, b)
   (array([
      [2, 2],
      [1, 1],
      [3, 3]
   ]), array([2, 1, 3]))

One other thought I had was this:

def shuffle_in_unison_scary(a, b):
   rng_state = numpy.random.get_state()
numpy.random.shuffle(a)
numpy.random.set_state(rng_state)
numpy.random.shuffle(b)

Suggestion : 2

We can also use the permutation() function inside the numpy.random library to create a randomized sequence of integers within a specified range in Python. This sequence can then be used as an index for both arrays to shuffle them.,This tutorial will introduce how to shuffle two NumPy arrays in Python., NumPy Shuffle Two Corresponding Arrays With the numpy.random.permutation() Function in Python , NumPy Shuffle Two Arrays With the sklearn.utils.shuffle() Function in Python

Suppose we have two arrays of the same length or same leading dimensions, and we want to shuffle them both in a way that the corresponding elements in both arrays remain corresponding. In that case, we can use the shuffle() function inside the sklean.utils library in Python. This shuffle() function takes the arrays as input parameters, shuffles them consistently, and returns a shuffled copy of each array. See the following code example.

import numpy as np
from sklearn
import utils

array1 = np.array([
   [0, 0],
   [1, 1],
   [2, 2]
])
array2 = np.array([0, 1, 2])

array1, array2 = utils.shuffle(array1, array2)
print(array1)
print(array2)

Output:

[
   [0 0]
   [2 2]
   [1 1]
]
[0 2 1]

If we don’t want to import the sklearn package and want to achieve the same goal as the previous one by using the NumPy package, we can use the shuffle() function inside the numpy.random library. This shuffle() function takes a sequence and randomizes it. We can then use this randomized sequence as an index for the two arrays to shuffle them. The following code example shows us how we can shuffle two arrays with the numpy.random.shuffle() function.

import numpy as np

array1 = np.array([
   [0, 0],
   [1, 1],
   [2, 2]
])
array2 = np.array([0, 1, 2])

randomize = np.arange(len(array2))

np.random.shuffle(randomize)

array1 = array1[randomize]
array2 = array2[randomize]
print(array1)
print(array2)

Suggestion : 3

The data of a2 and b2 is shared with c. To shuffle both arrays simultaneously, use numpy.random.shuffle(c).,In production code, you would of course try to avoid creating the original a and b at all and right away create c, a2 and b2.,Shuffle any number of arrays together, in-place, using only NumPy.,the two arrays x,y are now both randomly shuffled in the same way

This code works, and illustrates my goals:

def shuffle_in_unison(a, b):
   assert len(a) == len(b)
shuffled_a = numpy.empty(a.shape, dtype = a.dtype)
shuffled_b = numpy.empty(b.shape, dtype = b.dtype)
permutation = numpy.random.permutation(len(a))
for old_index, new_index in enumerate(permutation):
   shuffled_a[new_index] = a[old_index]
shuffled_b[new_index] = b[old_index]
return shuffled_a, shuffled_b

For example:

>>> a = numpy.asarray([
      [1, 1],
      [2, 2],
      [3, 3]
   ]) >>>
   b = numpy.asarray([1, 2, 3]) >>>
   shuffle_in_unison(a, b)
   (array([
      [2, 2],
      [1, 1],
      [3, 3]
   ]), array([2, 1, 3]))

One other thought I had was this:

def shuffle_in_unison_scary(a, b):
   rng_state = numpy.random.get_state()
numpy.random.shuffle(a)
numpy.random.set_state(rng_state)
numpy.random.shuffle(b)

Example: Let’s assume the arrays a and b look like this:

a = numpy.array([
   [
      [0., 1., 2.],
      [3., 4., 5.]
   ],

   [
      [6., 7., 8.],
      [9., 10., 11.]
   ],

   [
      [12., 13., 14.],
      [15., 16., 17.]
   ]
])

b = numpy.array([
   [0., 1.],
   [2., 3.],
   [4., 5.]
])

We can now construct a single array containing all the data:

c = numpy.c_[a.reshape(len(a), -1), b.reshape(len(b), -1)]
# array([
   [0., 1., 2., 3., 4., 5., 0., 1.],
   #[6., 7., 8., 9., 10., 11., 2., 3.],
   #[12., 13., 14., 15., 16., 17., 4., 5.]
])

Now we create views simulating the original a and b:

a2 = c[: ,: a.size 
      b2 = c[: , a.size 

def unison_shuffled_copies(a, b):
   assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]

Answer #3:

X = np.array([
   [1., 0.],
   [2., 1.],
   [0., 0.]
])
y = np.array([0, 1, 2])
from sklearn.utils
import shuffle
X, y = shuffle(X, y, random_state = 0)

Very simple solution:

randomize = np.arange(len(x))
np.random.shuffle(randomize)
x = x[randomize]
y = y[randomize]

Suggestion : 4

def unison_shuffled_copies(a, b):
   assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]

Suggestion : 5

I have two numpy arrays of different shapes, but with the same length (leading dimension). I want to shuffle each of them, such that corresponding elements continue to correspond — i.e. shuffle them in unison with respect to their leading indices.,Python – What are the differences between numpy arrays and matrices? Which one should I use,Python – Concatenating two one-dimensional NumPy arrays,Python – Comparing two NumPy arrays for equality, element-wise

This code works, and illustrates my goals:

def shuffle_in_unison(a, b):
   assert len(a) == len(b)
shuffled_a = numpy.empty(a.shape, dtype = a.dtype)
shuffled_b = numpy.empty(b.shape, dtype = b.dtype)
permutation = numpy.random.permutation(len(a))
for old_index, new_index in enumerate(permutation):
   shuffled_a[new_index] = a[old_index]
shuffled_b[new_index] = b[old_index]
return shuffled_a, shuffled_b

For example:

>>> a = numpy.asarray([
      [1, 1],
      [2, 2],
      [3, 3]
   ]) >>>
   b = numpy.asarray([1, 2, 3]) >>>
   shuffle_in_unison(a, b)
   (array([
      [2, 2],
      [1, 1],
      [3, 3]
   ]), array([2, 1, 3]))

One other thought I had was this:

def shuffle_in_unison_scary(a, b):
   rng_state = numpy.random.get_state()
numpy.random.shuffle(a)
numpy.random.set_state(rng_state)
numpy.random.shuffle(b)

def unison_shuffled_copies(a, b):
   assert len(a) == len(b)
p = numpy.random.permutation(len(a))
return a[p], b[p]

Suggestion : 6

I have two numpy arrays of different shapes, but with the same length (leading dimension). I want to shuffle each of them, such that corresponding elements continue to correspond -- i.e. shuffle them in unison with respect to their leading indices.,If you are not yet on Python 3.5 or need to write backward-compatible code, and you want this in a single expression, the most performant while the correct approach is to put it in a function:,This works...but it"s a little scary, as I see little guarantee it"ll continue to work -- it doesn"t look like the sort of thing that"s guaranteed to survive across numpy version, for example.,Check if one list is a subset of another in Python

This code works, and illustrates my goals:

def shuffle_in_unison(a, b):
   assert len(a) == len(b)
shuffled_a = numpy.empty(a.shape, dtype = a.dtype)
shuffled_b = numpy.empty(b.shape, dtype = b.dtype)
permutation = numpy.random.permutation(len(a))
for old_index, new_index in enumerate(permutation):
   shuffled_a[new_index] = a[old_index]
shuffled_b[new_index] = b[old_index]
return shuffled_a, shuffled_b

For example:

>>> a = numpy.asarray([
      [1, 1],
      [2, 2],
      [3, 3]
   ]) >>>
   b = numpy.asarray([1, 2, 3]) >>>
   shuffle_in_unison(a, b)
   (array([
      [2, 2],
      [1, 1],
      [3, 3]
   ]), array([2, 1, 3]))

One other thought I had was this:

def shuffle_in_unison_scary(a, b):
   rng_state = numpy.random.get_state()
numpy.random.shuffle(a)
numpy.random.set_state(rng_state)
numpy.random.shuffle(b)

I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The update() method would be what I need, if it returned its result instead of modifying a dictionary in-place.

>>> x = {
      "a": 1,
      "b": 2
   } >>>
   y = {
      "b": 10,
      "c": 11
   } >>>
   z = x.update(y) >>>
   print(z)
None
   >>>
   x {
      "a": 1,
      "b": 10,
      "c": 11
   }

In Python 3.9.0 or greater (released 17 October 2020): PEP-584, discussed here, was implemented and provides the simplest method:

z = x | y # NOTE: 3.9 + ONLY

In Python 3.5 or greater:

In Python 2, (or 3.4 or lower) write a function:

def merge_two_dicts(x, y):
   z = x.copy() # start with keys and values of x
z.update(y) # modifies z with keys and values of y
return z

and now:

z = merge_two_dicts(x, y)