Consequences of projecting data onto lower dimensions
1
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
na = 400
ma = [2, 1]
Sa = [[3, -2], [-2, 3]]
sigma1 = [3, 3]
nb = 400
mb = [8, 6]
Sb = [[3, -2], [-2, 3]]
xa, ya = np.random.multivariate_normal(ma, Sa, na).T
xb, yb = np.random.multivariate_normal(mb, Sb, nb).T
plt.plot(xa, ya, 'x')
plt.plot(xb, yb, 'x')
plt.axis('equal')
plt.show()
I have randomly generated data from 2-dimensional Gaussian Distributions and need to project this on w=[0, 1] and plot the histogram. I tried using plt.hist but it does not allow the multiplication.
python matplotlib
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
add a comment |
1
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
na = 400
ma = [2, 1]
Sa = [[3, -2], [-2, 3]]
sigma1 = [3, 3]
nb = 400
mb = [8, 6]
Sb = [[3, -2], [-2, 3]]
xa, ya = np.random.multivariate_normal(ma, Sa, na).T
xb, yb = np.random.multivariate_normal(mb, Sb, nb).T
plt.plot(xa, ya, 'x')
plt.plot(xb, yb, 'x')
plt.axis('equal')
plt.show()
I have randomly generated data from 2-dimensional Gaussian Distributions and need to project this on w=[0, 1] and plot the histogram. I tried using plt.hist but it does not allow the multiplication.
python matplotlib
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
see stats.stackexchange.com/questions/201921/…
– teng
Nov 24 '18 at 0:02
Thanks for the link it was very useful. My difficulty is how I am going to be able to break down the 2d array to be able to do the dot product as my data when np= 10 is like this. [array([ 1.63383494, 4.6541953 , 2.10788923, 2.46806161, 1.87287563, 0.76323836, 2.95160091, 1.74592451, -1.27726486, 4.22058637]), array([ 2.48245559, 1.57752103, -1.23525301, -1.76199059, -0.38459408, 1.78905969, -2.03621301, 1.23246001, 1.89331416, -0.71733151])]. I know that the first projection should come out as [11/5; 5.75/5] when taking w as [2;1]
– Michelle Abela
Nov 24 '18 at 0:54
you'll need to cast the arrays asnp.arrayand for dot product, usenp.dot.
– teng
Nov 24 '18 at 0:57
1
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Nov 24 '18 at 0:58
add a comment |
1
1
1
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
na = 400
ma = [2, 1]
Sa = [[3, -2], [-2, 3]]
sigma1 = [3, 3]
nb = 400
mb = [8, 6]
Sb = [[3, -2], [-2, 3]]
xa, ya = np.random.multivariate_normal(ma, Sa, na).T
xb, yb = np.random.multivariate_normal(mb, Sb, nb).T
plt.plot(xa, ya, 'x')
plt.plot(xb, yb, 'x')
plt.axis('equal')
plt.show()
I have randomly generated data from 2-dimensional Gaussian Distributions and need to project this on w=[0, 1] and plot the histogram. I tried using plt.hist but it does not allow the multiplication.
python matplotlib
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
na = 400
ma = [2, 1]
Sa = [[3, -2], [-2, 3]]
sigma1 = [3, 3]
nb = 400
mb = [8, 6]
Sb = [[3, -2], [-2, 3]]
xa, ya = np.random.multivariate_normal(ma, Sa, na).T
xb, yb = np.random.multivariate_normal(mb, Sb, nb).T
plt.plot(xa, ya, 'x')
plt.plot(xb, yb, 'x')
plt.axis('equal')
plt.show()
I have randomly generated data from 2-dimensional Gaussian Distributions and need to project this on w=[0, 1] and plot the histogram. I tried using plt.hist but it does not allow the multiplication.
python matplotlib
python matplotlib
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
edited Nov 24 '18 at 0:59
desertnaut
18.2k73872
18.2k73872
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
asked Nov 23 '18 at 23:44
Michelle AbelaMichelle Abela
134
134
see stats.stackexchange.com/questions/201921/…
– teng
Nov 24 '18 at 0:02
Thanks for the link it was very useful. My difficulty is how I am going to be able to break down the 2d array to be able to do the dot product as my data when np= 10 is like this. [array([ 1.63383494, 4.6541953 , 2.10788923, 2.46806161, 1.87287563, 0.76323836, 2.95160091, 1.74592451, -1.27726486, 4.22058637]), array([ 2.48245559, 1.57752103, -1.23525301, -1.76199059, -0.38459408, 1.78905969, -2.03621301, 1.23246001, 1.89331416, -0.71733151])]. I know that the first projection should come out as [11/5; 5.75/5] when taking w as [2;1]
– Michelle Abela
Nov 24 '18 at 0:54
you'll need to cast the arrays asnp.arrayand for dot product, usenp.dot.
– teng
Nov 24 '18 at 0:57
1
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Nov 24 '18 at 0:58
add a comment |
see stats.stackexchange.com/questions/201921/…
– teng
Nov 24 '18 at 0:02
Thanks for the link it was very useful. My difficulty is how I am going to be able to break down the 2d array to be able to do the dot product as my data when np= 10 is like this. [array([ 1.63383494, 4.6541953 , 2.10788923, 2.46806161, 1.87287563, 0.76323836, 2.95160091, 1.74592451, -1.27726486, 4.22058637]), array([ 2.48245559, 1.57752103, -1.23525301, -1.76199059, -0.38459408, 1.78905969, -2.03621301, 1.23246001, 1.89331416, -0.71733151])]. I know that the first projection should come out as [11/5; 5.75/5] when taking w as [2;1]
– Michelle Abela
Nov 24 '18 at 0:54
you'll need to cast the arrays asnp.arrayand for dot product, usenp.dot.
– teng
Nov 24 '18 at 0:57
1
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Nov 24 '18 at 0:58
see stats.stackexchange.com/questions/201921/…
– teng
Nov 24 '18 at 0:02
see stats.stackexchange.com/questions/201921/…
– teng
Nov 24 '18 at 0:02
Thanks for the link it was very useful. My difficulty is how I am going to be able to break down the 2d array to be able to do the dot product as my data when np= 10 is like this. [array([ 1.63383494, 4.6541953 , 2.10788923, 2.46806161, 1.87287563, 0.76323836, 2.95160091, 1.74592451, -1.27726486, 4.22058637]), array([ 2.48245559, 1.57752103, -1.23525301, -1.76199059, -0.38459408, 1.78905969, -2.03621301, 1.23246001, 1.89331416, -0.71733151])]. I know that the first projection should come out as [11/5; 5.75/5] when taking w as [2;1]
– Michelle Abela
Nov 24 '18 at 0:54
Thanks for the link it was very useful. My difficulty is how I am going to be able to break down the 2d array to be able to do the dot product as my data when np= 10 is like this. [array([ 1.63383494, 4.6541953 , 2.10788923, 2.46806161, 1.87287563, 0.76323836, 2.95160091, 1.74592451, -1.27726486, 4.22058637]), array([ 2.48245559, 1.57752103, -1.23525301, -1.76199059, -0.38459408, 1.78905969, -2.03621301, 1.23246001, 1.89331416, -0.71733151])]. I know that the first projection should come out as [11/5; 5.75/5] when taking w as [2;1]
– Michelle Abela
Nov 24 '18 at 0:54
you'll need to cast the arrays as
np.array and for dot product, use np.dot.– teng
Nov 24 '18 at 0:57
you'll need to cast the arrays as
np.array and for dot product, use np.dot.– teng
Nov 24 '18 at 0:57
1
1
Question has nothing to do with
machine-learning - kindly do not spam the tag (removed).– desertnaut
Nov 24 '18 at 0:58
Question has nothing to do with
machine-learning - kindly do not spam the tag (removed).– desertnaut
Nov 24 '18 at 0:58
add a comment |
1 Answer
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Below links may be useful for learning numpy.
https://docs.scipy.org/doc/numpy-1.15.0/user/basics.creation.html
https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-numpy-arrays.html
I think what you are asking is the below:
w = np.array([2,1])
a = np.array([xa,ya]).T
b = np.array([xb,yb]).T
aw = np.dot(a,w)
bw = np.dot(b,w)
plt.figure(0)
plt.hist(aw,label='a',histtype='step')
plt.hist(bw,label='b',histtype='step')
plt.title('projected')
plt.legend()
answered Nov 24 '18 at 1:07
tengteng
840721
add a comment |
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1 Answer
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Below links may be useful for learning numpy.
https://docs.scipy.org/doc/numpy-1.15.0/user/basics.creation.html
https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-numpy-arrays.html
I think what you are asking is the below:
w = np.array([2,1])
a = np.array([xa,ya]).T
b = np.array([xb,yb]).T
aw = np.dot(a,w)
bw = np.dot(b,w)
plt.figure(0)
plt.hist(aw,label='a',histtype='step')
plt.hist(bw,label='b',histtype='step')
plt.title('projected')
plt.legend()
answered Nov 24 '18 at 1:07
tengteng
840721
add a comment |
0
Below links may be useful for learning numpy.
https://docs.scipy.org/doc/numpy-1.15.0/user/basics.creation.html
https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-numpy-arrays.html
I think what you are asking is the below:
w = np.array([2,1])
a = np.array([xa,ya]).T
b = np.array([xb,yb]).T
aw = np.dot(a,w)
bw = np.dot(b,w)
plt.figure(0)
plt.hist(aw,label='a',histtype='step')
plt.hist(bw,label='b',histtype='step')
plt.title('projected')
plt.legend()
answered Nov 24 '18 at 1:07
tengteng
840721
add a comment |
0
0
0
Below links may be useful for learning numpy.
https://docs.scipy.org/doc/numpy-1.15.0/user/basics.creation.html
https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-numpy-arrays.html
I think what you are asking is the below:
w = np.array([2,1])
a = np.array([xa,ya]).T
b = np.array([xb,yb]).T
aw = np.dot(a,w)
bw = np.dot(b,w)
plt.figure(0)
plt.hist(aw,label='a',histtype='step')
plt.hist(bw,label='b',histtype='step')
plt.title('projected')
plt.legend()
answered Nov 24 '18 at 1:07
tengteng
840721
Below links may be useful for learning numpy.
https://docs.scipy.org/doc/numpy-1.15.0/user/basics.creation.html
https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-numpy-arrays.html
I think what you are asking is the below:
w = np.array([2,1])
a = np.array([xa,ya]).T
b = np.array([xb,yb]).T
aw = np.dot(a,w)
bw = np.dot(b,w)
plt.figure(0)
plt.hist(aw,label='a',histtype='step')
plt.hist(bw,label='b',histtype='step')
plt.title('projected')
plt.legend()
answered Nov 24 '18 at 1:07
tengteng
840721
answered Nov 24 '18 at 1:07
tengteng
840721
answered Nov 24 '18 at 1:07
tengteng
840721
answered Nov 24 '18 at 1:07
tengteng
840721
840721
add a comment |
add a comment |
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see stats.stackexchange.com/questions/201921/…
– teng
Nov 24 '18 at 0:02
Thanks for the link it was very useful. My difficulty is how I am going to be able to break down the 2d array to be able to do the dot product as my data when np= 10 is like this. [array([ 1.63383494, 4.6541953 , 2.10788923, 2.46806161, 1.87287563, 0.76323836, 2.95160091, 1.74592451, -1.27726486, 4.22058637]), array([ 2.48245559, 1.57752103, -1.23525301, -1.76199059, -0.38459408, 1.78905969, -2.03621301, 1.23246001, 1.89331416, -0.71733151])]. I know that the first projection should come out as [11/5; 5.75/5] when taking w as [2;1]
– Michelle Abela
Nov 24 '18 at 0:54
you'll need to cast the arrays as
np.arrayand for dot product, usenp.dot.– teng
Nov 24 '18 at 0:57
1
Question has nothing to do with
machine-learning- kindly do not spam the tag (removed).– desertnaut
Nov 24 '18 at 0:58