How to generate multi class test dataset using numpy?
0
I want to generate a multi class test dataset using numpy only for a classification problem.
For example X is a numpy array of dimension(mxn), y of dimension(mx1) and let's say there are k no. of classes. Please help me with the code.
[Here X represents the features and y represents the labels]
python-3.x numpy random classification knn
asked Nov 21 at 6:53
Amartya K
42
add a comment |
0
I want to generate a multi class test dataset using numpy only for a classification problem.
For example X is a numpy array of dimension(mxn), y of dimension(mx1) and let's say there are k no. of classes. Please help me with the code.
[Here X represents the features and y represents the labels]
python-3.x numpy random classification knn
asked Nov 21 at 6:53
Amartya K
42
Check out make_classification from scikit-learn. You can specify the size of arrays and number of classes in that, and also they will be somewhat appropriate. It doesnt meet your demand of only numpy as you have to install scikit-learn, but internally it still uses numpy. So maybe you can make something out of the source code.
– Vivek Kumar
Nov 22 at 8:28
add a comment |
0
0
0
I want to generate a multi class test dataset using numpy only for a classification problem.
For example X is a numpy array of dimension(mxn), y of dimension(mx1) and let's say there are k no. of classes. Please help me with the code.
[Here X represents the features and y represents the labels]
python-3.x numpy random classification knn
asked Nov 21 at 6:53
Amartya K
42
I want to generate a multi class test dataset using numpy only for a classification problem.
For example X is a numpy array of dimension(mxn), y of dimension(mx1) and let's say there are k no. of classes. Please help me with the code.
[Here X represents the features and y represents the labels]
python-3.x numpy random classification knn
python-3.x numpy random classification knn
asked Nov 21 at 6:53
Amartya K
42
asked Nov 21 at 6:53
Amartya K
42
asked Nov 21 at 6:53
Amartya K
42
asked Nov 21 at 6:53
Amartya K
42
asked Nov 21 at 6:53
Amartya K
42
42
Check out make_classification from scikit-learn. You can specify the size of arrays and number of classes in that, and also they will be somewhat appropriate. It doesnt meet your demand of only numpy as you have to install scikit-learn, but internally it still uses numpy. So maybe you can make something out of the source code.
– Vivek Kumar
Nov 22 at 8:28
add a comment |
Check out make_classification from scikit-learn. You can specify the size of arrays and number of classes in that, and also they will be somewhat appropriate. It doesnt meet your demand of only numpy as you have to install scikit-learn, but internally it still uses numpy. So maybe you can make something out of the source code.
– Vivek Kumar
Nov 22 at 8:28
Check out make_classification from scikit-learn. You can specify the size of arrays and number of classes in that, and also they will be somewhat appropriate. It doesnt meet your demand of only numpy as you have to install scikit-learn, but internally it still uses numpy. So maybe you can make something out of the source code.
– Vivek Kumar
Nov 22 at 8:28
Check out make_classification from scikit-learn. You can specify the size of arrays and number of classes in that, and also they will be somewhat appropriate. It doesnt meet your demand of only numpy as you have to install scikit-learn, but internally it still uses numpy. So maybe you can make something out of the source code.
– Vivek Kumar
Nov 22 at 8:28
add a comment |
1 Answer
1
active
oldest
votes
1
You can use np.random.randint like:
import numpy as np
m = 4
n = 4
k = 5
X = np.random.randint(0,2,(m,n))
X
array([[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 0, 0],
[1, 1, 1, 1]])
y = np.random.randint(0,k,m)
y
array([3, 3, 0, 4])
answered Nov 21 at 9:42
Franco Piccolo
1,531611
I tried this but this is a bit too random. I need to generate points for multi class. Like some points will represent a class or group and they should be near each other. By near I mean the Euclidean distance. For eg. you're testing KNN algorithm with this dataset but since it doesn't represent the classes properly so you can't use it.
– Amartya K
Nov 21 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 at 10:47
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
You can use np.random.randint like:
import numpy as np
m = 4
n = 4
k = 5
X = np.random.randint(0,2,(m,n))
X
array([[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 0, 0],
[1, 1, 1, 1]])
y = np.random.randint(0,k,m)
y
array([3, 3, 0, 4])
answered Nov 21 at 9:42
Franco Piccolo
1,531611
I tried this but this is a bit too random. I need to generate points for multi class. Like some points will represent a class or group and they should be near each other. By near I mean the Euclidean distance. For eg. you're testing KNN algorithm with this dataset but since it doesn't represent the classes properly so you can't use it.
– Amartya K
Nov 21 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 at 10:47
add a comment |
1
You can use np.random.randint like:
import numpy as np
m = 4
n = 4
k = 5
X = np.random.randint(0,2,(m,n))
X
array([[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 0, 0],
[1, 1, 1, 1]])
y = np.random.randint(0,k,m)
y
array([3, 3, 0, 4])
answered Nov 21 at 9:42
Franco Piccolo
1,531611
I tried this but this is a bit too random. I need to generate points for multi class. Like some points will represent a class or group and they should be near each other. By near I mean the Euclidean distance. For eg. you're testing KNN algorithm with this dataset but since it doesn't represent the classes properly so you can't use it.
– Amartya K
Nov 21 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 at 10:47
add a comment |
1
1
1
You can use np.random.randint like:
import numpy as np
m = 4
n = 4
k = 5
X = np.random.randint(0,2,(m,n))
X
array([[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 0, 0],
[1, 1, 1, 1]])
y = np.random.randint(0,k,m)
y
array([3, 3, 0, 4])
answered Nov 21 at 9:42
Franco Piccolo
1,531611
You can use np.random.randint like:
import numpy as np
m = 4
n = 4
k = 5
X = np.random.randint(0,2,(m,n))
X
array([[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 0, 0],
[1, 1, 1, 1]])
y = np.random.randint(0,k,m)
y
array([3, 3, 0, 4])
answered Nov 21 at 9:42
Franco Piccolo
1,531611
answered Nov 21 at 9:42
Franco Piccolo
1,531611
answered Nov 21 at 9:42
Franco Piccolo
1,531611
answered Nov 21 at 9:42
Franco Piccolo
1,531611
1,531611
I tried this but this is a bit too random. I need to generate points for multi class. Like some points will represent a class or group and they should be near each other. By near I mean the Euclidean distance. For eg. you're testing KNN algorithm with this dataset but since it doesn't represent the classes properly so you can't use it.
– Amartya K
Nov 21 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 at 10:47
add a comment |
I tried this but this is a bit too random. I need to generate points for multi class. Like some points will represent a class or group and they should be near each other. By near I mean the Euclidean distance. For eg. you're testing KNN algorithm with this dataset but since it doesn't represent the classes properly so you can't use it.
– Amartya K
Nov 21 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 at 10:47
I tried this but this is a bit too random. I need to generate points for multi class. Like some points will represent a class or group and they should be near each other. By near I mean the Euclidean distance. For eg. you're testing KNN algorithm with this dataset but since it doesn't represent the classes properly so you can't use it.
– Amartya K
Nov 21 at 10:36
I tried this but this is a bit too random. I need to generate points for multi class. Like some points will represent a class or group and they should be near each other. By near I mean the Euclidean distance. For eg. you're testing KNN algorithm with this dataset but since it doesn't represent the classes properly so you can't use it.
– Amartya K
Nov 21 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 at 10:43
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 at 10:47
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 at 10:47
add a comment |
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Check out make_classification from scikit-learn. You can specify the size of arrays and number of classes in that, and also they will be somewhat appropriate. It doesnt meet your demand of only numpy as you have to install scikit-learn, but internally it still uses numpy. So maybe you can make something out of the source code.
– Vivek Kumar
Nov 22 at 8:28