I'm trying to use the CVXOPT qp solver to compute the Lagrange Multipliers for a Support Vector Machine
def svm(X, Y, c):
      m = len(X)
      P = matrix(np.dot(Y, Y.T) * np.dot(X, X.T))
      q = matrix(np.ones(m) * -1)
      g1 = np.asarray(np.diag(np.ones(m) * -1))
      g2 = np.asarray(np.diag(np.ones(m)))
      G = matrix(np.append(g1, g2, axis=0))
      h = matrix(np.append(np.zeros(m), (np.ones(m) * c), axis =0))
      A = np.reshape((Y.T), (1,m))
      b = matrix([0])
      print (A).shape
      A = matrix(A)
      sol = solvers.qp(P, q, G, h, A, b)
      print sol
Here X is a 1000 X 2 matrix and Y has the same number of labels. The solver throws the following error:
$ python svm.py 
(1, 1000)
Traceback (most recent call last):
  File "svm.py", line 35, in <module>
    svm(X, Y, 50)
  File "svm.py", line 29, in svm
    sol = solvers.qp(P, q, G, h, A, b)
  File "/usr/local/lib/python2.7/site-packages/cvxopt/coneprog.py", line 4468, in qp
    return coneqp(P, q, G, h, None, A,  b, initvals, options = options)
  File "/usr/local/lib/python2.7/site-packages/cvxopt/coneprog.py", line 1914, in coneqp
    %q.size[0])
TypeError: 'A' must be a 'd' matrix with 1000 columns
I printed the shape of A and it's a (1,1000) matrix after reshaping from a vector. What exactly is causing this error?
Your matrix elements have to be of the floating-point type as well. So the error is removed by using A = A.astype('float') to cast it.
i have try A=A.astype(double) to solve it, but it is invalid, since python doesn't know what double is or A has no method astype.
via using 
A = matrix(A, (1, m), 'd')
could actually solve this problem!
The error - "TypeError: 'A' must be a 'd' matrix with 1000 columns:" has two condition namely:
d' A.size[1] != c.size[0]. Check for these conditions.
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