1 / 14

numpy

numpy. Numpy objects. >>> import numpy as np >>> np.array([1,2,3]) ‏ array([1, 2, 3]) ‏ >>> np.array([1,2,3.0]) ‏ array([ 1., 2., 3.]) ‏ >>> np.array([1,2,3],np.float64) ‏ array([ 1., 2., 3.]) ‏ >>> np.array([range(3) for x in range(4)],np.float64) ‏ array([[ 0., 1., 2.],

didina
Télécharger la présentation

numpy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. numpy

  2. Numpy objects >>> import numpy as np >>> np.array([1,2,3])‏ array([1, 2, 3])‏ >>> np.array([1,2,3.0])‏ array([ 1., 2., 3.])‏ >>> np.array([1,2,3],np.float64)‏ array([ 1., 2., 3.])‏ >>> np.array([range(3) for x in range(4)],np.float64)‏ array([[ 0., 1., 2.], [ 0., 1., 2.], [ 0., 1., 2.], [ 0., 1., 2.]])‏ >>>

  3. Numpy objects >>> A= np.array([range(3) for x in range(4)],np.float64)‏ >>> np.matrix([range(3) for x in range(4)],np.float64)‏ matrix([[ 0., 1., 2.], [ 0., 1., 2.], [ 0., 1., 2.], [ 0., 1., 2.]])‏ >>> M= np.matrix([range(3) for x in range(4)],np.float64)‏ >>> type(A), type(M)‏ (<type 'numpy.ndarray'>, <class 'numpy.core.defmatrix.matrix'>)‏ >>> np.matrix(range(3),np.float64)‏ matrix([[ 0., 1., 2.]])‏ >>> np.array(range(3),np.float64)‏ array([ 0., 1., 2.])‏ >>> M=np.matrix(range(3),np.float64)‏ >>> A=np.array(range(3),np.float64)‏ >>> A.shape (3,)‏ >>> M.shape (1, 3)‏

  4. Numpy objects >>> M=np.matrix(range(3),np.float64)‏ >>> A=np.array(range(3),np.float64)‏ >>> A.T array([ 0., 1., 2.])‏ >>> M.T matrix([[ 0.], [ 1.], [ 2.]])‏ >>> A=np.array([[1,2],[2,-2]],np.float64)‏ >>> M=np.matrix([[1,2],[2,-2]],np.float64)‏ >>> M.T matrix([[ 1., 2.], [ 2., -2.]])‏ >>> A.T array([[ 1., 2.], [ 2., -2.]])‏ >>> A.I Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'numpy.ndarray' object has no attribute 'I' >>> M.I matrix([[ 0.33333333, 0.33333333], [ 0.33333333, -0.16666667]])‏ >>>

  5. Numpy objects >>> M matrix([[ 1., 2.], [ 2., -2.]])‏ >>> M.I matrix([[ 0.33333333, 0.33333333], [ 0.33333333, -0.16666667]])‏ >>>np.linalg.inv(A)‏ array([[ 0.33333333, 0.33333333], [ 0.33333333, -0.16666667]])‏ >>> >>> M2=np.matrix([[1,1],[0,0]],np.float64)‏ >>> A2=np.array([[1,1],[0,0]],np.float64)‏ >>> M*M2 matrix([[ 1., 1.], [ 2., 2.]])‏ >>> A*A2 array([[ 1., 2.], [ 0., -0.]])‏ >>> A2*A array([[ 1., 2.], [ 0., -0.]])‏ >>> M2*M matrix([[ 3., 0.], [ 0., 0.]])‏

  6. Numpy objects >>> A == M matrix([[ True, True], [ True, True]], dtype=bool)‏ >>> Ai=np.linalg.inv(A)‏ >>> np.dot(A,Ai)‏ array([[ 1.00000000e+00, 5.55111512e-17], [ 0.00000000e+00, 1.00000000e+00]])‏ >>> np.dot(Ai,A)‏ array([[ 1.00000000e+00, -1.11022302e-16], [ 0.00000000e+00, 1.00000000e+00]])‏ >>> M*M.I matrix([[ 1.00000000e+00, 5.55111512e-17], [ 0.00000000e+00, 1.00000000e+00]])‏ >>> M.I*M matrix([[ 1.00000000e+00, -1.11022302e-16], [ 0.00000000e+00, 1.00000000e+00]])‏

  7. Numpy objects >>> np.random.uniform()‏ 0.70420105296872415 >>> np.random.uniform(-0.1,0.1)‏ -0.059314492060574403 >>> np.random.uniform(-0.1,0.1,4)‏ array([ 0.06606591, 0.07766084, 0.08635536, 0.08010191])‏ >>> np.random.uniform(-0.1,0.1,(2,2))‏ array([[-0.06448909, 0.07906606], [-0.04752628, -0.02955906]])‏ >>> np.random.normal(-1,1,(3,2))‏ array([[-0.32922971, -0.05700329], [-2.81944081, 0.43708656], [-1.4274894 , -0.61651697]])‏ >>> >>> np.sort([1,5.9,2,-1])‏ array([-1. , 1. , 2. , 5.9])‏ >>> np.argsort([1,5.9,2,-1])‏ array([3, 0, 2, 1])‏ >>>

  8. Numpy objects >>>np.ones((4,4))‏ array([[ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.]])‏ >>> np.zeros((4,4))‏ array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]])‏ >>> A=np.zeros((4,4))‏ >>> for i in range(len(A)): ... A[i][:]=np.random.uniform(-10,10,len(A))‏ ... >>> A array([[-2.74761648, -3.81873152, 8.5612057 , -6.50432488], [ 4.04987279, -9.37231031, -7.96381121, 0.4987925 ], [-5.86088041, 7.90005728, 7.30370647, 8.57564479], [ 9.09765827, 2.85531062, 9.41611209, -8.07463238]])‏ >>> for i in range(len(A)): ... A[i,:]=np.random.uniform(-10,10,len(A))‏ ...

  9. Numpy objects >>> B array([[ 0., 1.], [ 1., 0.]])‏ >>> evals,evecs=np.linalg.eig(B)‏ >>> evals array([ 1., -1.])‏ >>> for i in range(len(B)): ... print evals[i],evecs[i] ... 1.0 [ 0.70710678 -0.70710678] -1.0 [ 0.70710678 0.70710678] >>>

  10. Numpy objects >>> A+A.T array([[ -9.87072296, -8.6819582 , 1.05438839, 0.77621665], [ -8.6819582 , -6.20131579, 12.1133606 , -4.29184743], [ 1.05438839, 12.1133606 , 13.24488407, -7.21060701], [ 0.77621665, -4.29184743, -7.21060701, 19.57503177]])‏ >>> A=A+A.T >>> evals,evecs=np.linalg.eig(A)‏ >>> evals array([-19.38912856, -2.79317213, 27.87492659, 11.05525118])‏

  11. Numpy objects >>> inputs=np.array([[ 1.],[ 2.],[ 3.],[ 4.],[ 5.]])‏ >>> targets=np.array([[ 3.],[ 5.],[ 7.],[ 9.],[ 11.]])‏ >>> np.concatenate((inputs,targets))‏ array([[ 1.], [ 2.], [ 3.], [ 4.], [ 5.], [ 3.], [ 5.], [ 7.], [ 9.], [ 11.]])‏ >>> np.concatenate((inputs,targets),axis=1)‏ array([[ 1., 3.], [ 2., 5.], [ 3., 7.], [ 4., 9.], [ 5., 11.]])‏ >>> it=np.concatenate((inputs,targets),axis=1)‏

  12. Numpy objects >>> print np.sum(it), it.size 50.0 10 >>> print np.mean(it), np.sum(it)/it.size 5.0 5.0 >>> np.sum(it,axis=0), np.sum(it,axis=1)‏ (array([ 15., 35.]), array([ 4., 7., 10., 13., 16.]))‏ >>> len(it), len(it[0])‏ (5, 2)‏ >>> np.mean(it,axis=0),np.mean(it,axis=1)‏ (array([ 3., 7.]), array([ 2. , 3.5, 5. , 6.5, 8. ]))‏ >>>

  13. Numpy objects >>> D=np.zeros((20,3))‏ >>> for i in range(len(D)): ... D[i,:]=np.random.uniform(-10,10,len(D[i]))‏ >>> D.mean(axis=0)‏ array([-1.02909335, 2.77430392, -1.00094332])‏ >>> >>> np.std(D)‏ 5.8341355973553766 >>> np.std(D,axis=0)‏ array([ 6.37529467, 5.64920856, 4.46994818])‏ >>> np.var(D,axis=0)‏ array([ 40.64438211, 31.91355732, 19.98043675])‏ >>> np.sqrt(np.var(D,axis=0))‏ array([ 6.37529467, 5.64920856, 4.46994818])‏ >>>

  14. Numpy objects >>> r=0.1 >>> a=np.array(range(4),np.float)+np.random.uniform(-r,r)‏ >>> b=np.array(range(4),np.float)+np.random.uniform(-r,r) -5 >>> c=np.array(range(4,0,-1),np.float)+np.random.uniform(-r,r) -5 >>> d=np.matrix([np.random.uniform(-r,r) for i in range(4)],np.float)‏ >>> np.cov(a,a)‏ array([[ 1.66666667, 1.66666667], [ 1.66666667, 1.66666667]])‏ >>> np.cov(a,b)‏ array([[ 1.66666667, 1.66666667], [ 1.66666667, 1.66666667]])‏ >>> np.cov(a,c)‏ array([[ 1.66666667, -1.66666667], [-1.66666667, 1.66666667]])‏ >>> np.cov(a,d)‏ array([[ 1.66666667, -0.01641726], [-0.01641726, 0.0033877 ]])‏

More Related