Difference between revisions of "Numpy linalg"

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print np.einsum('ji', a)
 
print np.einsum('ji', a)
  
print "np.einsum('ji', a), dot product"
+
print "np.einsum('ij,jk', a, a), dot product"
 
print np.einsum('ij,jk', a, a)
 
print np.einsum('ij,jk', a, a)
 
print np.dot(a, a)
 
print np.dot(a, a)
 
</source>
 
</source>

Revision as of 11:54, 19 June 2014

How to transpose higher dimension arrays

http://jameshensman.wordpress.com/2010/06/14/multiple-matrix-multiplication-in-numpy/

Faster dot product using BLAS

http://www.huyng.com/posts/faster-numpy-dot-product/

http://stackoverflow.com/questions/5990577/speeding-up-numpy-dot

http://wiki.scipy.org/PerformanceTips

http://thread.gmane.org/gmane.comp.python.numeric.general/28135/

Multi dot

http://wiki.scipy.org/Cookbook/MultiDot

Einsum

http://chintaksheth.wordpress.com/2013/07/31/numpy-the-tricks-of-the-trade-part-ii/

http://stackoverflow.com/questions/14758283/is-there-a-numpy-scipy-dot-product-calculating-only-the-diagonal-entries-of-the

a = np.arange(4).reshape(2,2)
print a
print "np.einsum('ii', a), row i multiplied downwards"
print np.einsum('ii', a)

print "np.einsum('ij', a), same matrix ?"
print np.einsum('ij', a)

print "np.einsum('ji', a), transpose"
print np.einsum('ji', a)

print "np.einsum('ij,jk', a, a), dot product"
print np.einsum('ij,jk', a, a)
print np.dot(a, a)