Lines Matching refs:np
2 import numpy as np namespace
17 tmp = np.copy(a[j,:])
18 a[j,:] = np.copy(a[k,:])
23 tmp = np.copy(a[:,j])
24 a[:,j] = np.copy(a[:,k])
30 ma = np.copy(src)
34 piv=np.zeros(len(ma),dtype=int)
42 d=np.diagonal(ma)
43 j = np.argmax(d[k:]) + k
50 v = np.copy(ma[k+1:,k])
60 ma[k+1:,k+1:] = ma[k+1:,k+1:] - np.matmul(v , np.transpose(v)) / alpha
65 diags=(np.array(range(0,k),dtype=int),np.array(range(0,k),dtype=int))
67 diags=(np.array(range(0,k+1),dtype=int),np.array(range(0,k+1),dtype=int))
69 ll=np.tril(ma)
72 d=np.diag(np.diagonal(ma))
79 p=np.identity(n)
83 a = np.matmul(p,np.matmul(src,np.transpose(p)))
84 t = np.matmul(ll,np.matmul(d,np.transpose(ll)))
87 return(np.all(r == 0.0))
97 data = np.random.randn(2*nb)
99 data_comp = data.view(dtype=np.complex128)
104 return(a.reshape(np.size(a)).view(dtype=np.float64))
111 data1=np.random.randn(NBSAMPLESA)
112 data2=np.random.randn(NBSAMPLESB)
144 ma = np.copy(data1[0:a*b]).reshape(a,b)
145 mb = np.copy(data2[0:b*c]).reshape(b,c)
146 r = np.matmul(ma , mb)
155 ma = np.copy(data1C[0:a*b]).reshape(a,b)
156 mb = np.copy(data2C[0:b*c]).reshape(b,c)
157 r = np.matmul(ma , mb)
165 m = list(np.identity(d))
667 return(np.array(m))
670 a = 1.0 * np.diag(np.array(range(1,d+1)))/d
672 return(np.matmul(p,np.matmul(a,np.transpose(p))))
676 a = np.diag(np.hstack([np.array(range(1,d+1-k)),np.zeros(k)])) / d
678 a = 1.0 * np.diag(np.array(range(1,d+1)))/d
680 return(np.matmul(p,np.matmul(a,np.transpose(p))))
693 data1=np.random.randn(NBSAMPLES)
703 data2=np.random.randn(NBSAMPLES)
706 vecdata=np.random.randn(NBVECSAMPLES)
734 ma = np.copy(data1[0:a*b]).reshape(a,b)
735 mb = np.copy(data2[0:a*b]).reshape(a,b)
743 ma = np.copy(data1[0:a*b]).reshape(a,b)
744 v = np.copy(vecdata[0:b])
752 ma = np.copy(data1[0:a*b]).reshape(a,b)
753 mb = np.copy(data2[0:a*b]).reshape(a,b)
761 ma = np.copy(data1[0:a*b]).reshape(a,b)
762 r = np.transpose(ma)
769 ma = np.copy(data1C[0:a*b]).reshape(a,b)
770 r = np.transpose(ma)
777 ma = np.copy(data1[0:a*b]).reshape(a,b)
804 ma = np.array([[0., 3.], [4., 5.]])
806 r = np.linalg.inv(ma)
812 ma = np.array([[1., 2.], [2., 4.]])
815 r = np.array([0.,0.5,1.0,-0.5])
855 a = np.random.randn(d*d)
862 utinv = np.linalg.solve(ut,a)
863 ltinv = np.linalg.solve(lt,a)
864 cholinv = np.linalg.solve(ma,a)
892 r=np.array([(4,4),(8,8),(9,9),(15,15),(16,16)])
937 matrix=np.random.randn(matrixDim * matrixDim).reshape(matrixDim,matrixDim)
940 matrixLT = np.tril(matrix)
941 diagvalues=[notnull(x) for x in np.diagonal(matrixLT)]
942 np.fill_diagonal(matrixLT, diagvalues)
944 matrixUT = np.triu(matrix)
945 diagvalues=[notnull(x) for x in np.diagonal(matrixUT)]
946 np.fill_diagonal(matrixUT, diagvalues)
952 vector=np.random.randn(matrixDim * cols)
955 vector = np.array(vector).reshape(matrixDim,cols)
958 refLT=np.linalg.solve(matrixLT,vector)
961 refUT=np.linalg.solve(matrixUT,vector)
964 thedims=list(np.array(thedims).reshape(2*nb))
991 x = np.random.randn(s)
997 theInput += list(np.array(x).reshape(s))
998 theOutputVector += list(np.array(v).reshape(s))
1002 x = np.array([1,0,0,0])
1007 theInput += list(np.array(x).reshape(s))
1008 theOutputVector += list(np.array(v).reshape(s))
1012 x = np.array([-1,0,0,0])
1017 theInput += list(np.array(x).reshape(s))
1018 theOutputVector += list(np.array(v).reshape(s))
1031 np.fill_diagonal(product,0)
1033 return (np.all(np.abs(product)<1e-10))
1043 nm = np.dot(q,r)
1049 if not (np.allclose(nm,m,rtol=rtol,atol=atol)):
1050 print(np.max(np.abs(nm-m)))
1051 print(np.max(atol + rtol * np.abs(m)))
1052 assert (np.allclose(nm,m,rtol=rtol,atol=atol))
1084 theMatrix += list(np.array(m).reshape(rows*cols))
1114 theRefTau += list(np.array(tau).reshape(cols))
1115 theRefR += list(np.array(r).reshape(rows*cols))
1116 theRefQ += list(np.array(q).reshape(rows*rows))
1122 theMatrix += list(np.array(m).reshape(d*d))
1138 theRefTau += list(np.array(tau).reshape(d))
1139 theRefR += list(np.array(r).reshape(d*d))
1140 theRefQ += list(np.array(q).reshape(d*d))
1157 v=v[np.newaxis]
1159 p=np.identity(n)-beta * v.T .dot(v)