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 '18 at 6:53
Amartya KAmartya 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 '18 at 6:53
Amartya KAmartya 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 '18 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 '18 at 6:53
Amartya KAmartya 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 '18 at 6:53
Amartya KAmartya K
42
asked Nov 21 '18 at 6:53
Amartya KAmartya K
42
asked Nov 21 '18 at 6:53
Amartya KAmartya K
42
asked Nov 21 '18 at 6:53
Amartya KAmartya K
42
asked Nov 21 '18 at 6:53
Amartya KAmartya 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 '18 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 '18 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 '18 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 '18 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 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
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 '18 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 '18 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 '18 at 10:47
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53406682%2fhow-to-generate-multi-class-test-dataset-using-numpy%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
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 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
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 '18 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 '18 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 '18 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 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
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 '18 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 '18 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 '18 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 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
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 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
answered Nov 21 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
answered Nov 21 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
answered Nov 21 '18 at 9:42
Franco PiccoloFranco Piccolo
1,591716
1,591716
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 '18 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 '18 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 '18 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 '18 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 '18 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 '18 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 '18 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 '18 at 10:36
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 '18 at 10:43
I don't understand your requirements, maybe you can clarify what kind of output you are expecting.
– Franco Piccolo
Nov 21 '18 at 10:43
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 '18 at 10:47
Something like this in.mathworks.com/matlabcentral/mlc-downloads/downloads/…
– Amartya K
Nov 21 '18 at 10:47
add a comment |
draft saved
draft discarded
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53406682%2fhow-to-generate-multi-class-test-dataset-using-numpy%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
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 '18 at 8:28