from cvxopt.solvers import qp as QP
from random import sample
from cvxopt import matrix as m
from numpy import tanh, exp, mean, vstack, hstack, ones, zeros, matrix, random, linalg, arange, eye
import numpy.linalg as linalg

def lin(x,y,args=None):
    return (x*y.T)[0,0]

def lap(x,y,sigma):
    return exp(-sigma*linalg.norm(x-y))

def gauss(x,y,sigma):
    return exp(-sigma**2*linalg.norm(x-y)**2)

def poly(x,y,d):
    a = 1.0
    return (a*(x*y.T)[0,0])**d

def sigmoid(x,y,a):
    c =0
    return tanh(a*((x*y.T)[0,0])+c)

def comb_sum(x,y,sigma):
    return lin(x,y) + gauss(x,y,sigma)

def comb_poly(x,y,sig):
    return poly(x,y,3)+gauss(x,y,sig)

def comb_wsum(x,y,a):
    return (a**2)*lin(x,y)+(1-a)**2*gauss(x,y)

def comb_prod(x,y,args):
    return lin(x,y)*gauss(x,y, args)

class SVM:

    def __init__(self,X,Y,error):
        self.X = X
        self.Y = Y
        self.a = None
        self.e = error
        self.predict = None

    def sample(self):
        X = self.X
        Y = self.Y
        XY = zip(X,Y)
        smpl = [XY[i] for i in sample(xrange(len(XY)),200)]
        self.X, self.Y =zip(*smpl)

    def solve(self, kern=lin, args=None):
        X = self.X
        Y = self.Y
        e = self.e
       # OBX = matrix([[0.0008,3.0]
       #          ,[2,20.0]
       #          ,[0.0004,1]
       #          ,[5,0]])
       # Y = matrix([0.0,2.0,1.0,3.0]).T
       # X = matrix(data.XY[0],dtype='float')
       # Y = matrix(data.XY[1],dtype='float').T
        #trim the datapoints before passing to svm
        X = matrix(X, dtype='float')
        Y = matrix(Y, dtype='float').T
        C = 1
        N = len(X)
        E = ones((N,1))
        E2 = vstack((E, E))
        K = matrix([[kern(x,y,args) for x in X] for y in X])
        #K = X*X.T
        P = vstack((hstack(( K,-K))
                  , hstack((-K, K))))
        #a trick to increase the rank of P without perturbing data too much
        #P = P + np.eye(2*N)*1e-10
        q = vstack((e*E-Y, e*E+Y))

        A = vstack((E, -E)).T
        I = eye(2*N)
        G = vstack((-I, I))
        h = vstack((0*E2,C*E2))
        b = 0.0
        sol = QP(m(P), m(q), G=m(G), h=m(h), A=m(A), b =m(b))
        self.a = matrix(sol['x'])
        st = sol['status']


        def predict (x):
            b = mean(Y - e - self.a.T * hstack((K,-K)).T)
            k = matrix([[kern(x,y,args)] for y in X])
            return self.a.T * vstack((k,-k)) + b

        self.predict = predict
        return st


if __name__ == "__main__":
    args = []
    for term in sys.argv[1:len(sys.argv)]:
        args.append(unicode(term))
    l = LFSC(args)
    solve(l)

ca33364cd79c3abdd1ebcc4d379d940d0322961a
<mask token>


def lin(x, y, args=None):
    return (x * y.T)[0, 0]


<mask token>


def gauss(x, y, sigma):
    return exp(-sigma ** 2 * linalg.norm(x - y) ** 2)


<mask token>


class SVM:

    def __init__(self, X, Y, error):
        self.X = X
        self.Y = Y
        self.a = None
        self.e = error
        self.predict = None

    def sample(self):
        X = self.X
        Y = self.Y
        XY = zip(X, Y)
        smpl = [XY[i] for i in sample(xrange(len(XY)), 200)]
        self.X, self.Y = zip(*smpl)

    def solve(self, kern=lin, args=None):
        X = self.X
        Y = self.Y
        e = self.e
        X = matrix(X, dtype='float')
        Y = matrix(Y, dtype='float').T
        C = 1
        N = len(X)
        E = ones((N, 1))
        E2 = vstack((E, E))
        K = matrix([[kern(x, y, args) for x in X] for y in X])
        P = vstack((hstack((K, -K)), hstack((-K, K))))
        q = vstack((e * E - Y, e * E + Y))
        A = vstack((E, -E)).T
        I = eye(2 * N)
        G = vstack((-I, I))
        h = vstack((0 * E2, C * E2))
        b = 0.0
        sol = QP(m(P), m(q), G=m(G), h=m(h), A=m(A), b=m(b))
        self.a = matrix(sol['x'])
        st = sol['status']

        def predict(x):
            b = mean(Y - e - self.a.T * hstack((K, -K)).T)
            k = matrix([[kern(x, y, args)] for y in X])
            return self.a.T * vstack((k, -k)) + b
        self.predict = predict
        return st


<mask token>

<mask token>


def lin(x, y, args=None):
    return (x * y.T)[0, 0]


<mask token>


def gauss(x, y, sigma):
    return exp(-sigma ** 2 * linalg.norm(x - y) ** 2)


<mask token>


def comb_sum(x, y, sigma):
    return lin(x, y) + gauss(x, y, sigma)


def comb_poly(x, y, sig):
    return poly(x, y, 3) + gauss(x, y, sig)


<mask token>


def comb_prod(x, y, args):
    return lin(x, y) * gauss(x, y, args)


class SVM:

    def __init__(self, X, Y, error):
        self.X = X
        self.Y = Y
        self.a = None
        self.e = error
        self.predict = None

    def sample(self):
        X = self.X
        Y = self.Y
        XY = zip(X, Y)
        smpl = [XY[i] for i in sample(xrange(len(XY)), 200)]
        self.X, self.Y = zip(*smpl)

    def solve(self, kern=lin, args=None):
        X = self.X
        Y = self.Y
        e = self.e
        X = matrix(X, dtype='float')
        Y = matrix(Y, dtype='float').T
        C = 1
        N = len(X)
        E = ones((N, 1))
        E2 = vstack((E, E))
        K = matrix([[kern(x, y, args) for x in X] for y in X])
        P = vstack((hstack((K, -K)), hstack((-K, K))))
        q = vstack((e * E - Y, e * E + Y))
        A = vstack((E, -E)).T
        I = eye(2 * N)
        G = vstack((-I, I))
        h = vstack((0 * E2, C * E2))
        b = 0.0
        sol = QP(m(P), m(q), G=m(G), h=m(h), A=m(A), b=m(b))
        self.a = matrix(sol['x'])
        st = sol['status']

        def predict(x):
            b = mean(Y - e - self.a.T * hstack((K, -K)).T)
            k = matrix([[kern(x, y, args)] for y in X])
            return self.a.T * vstack((k, -k)) + b
        self.predict = predict
        return st


<mask token>

<mask token>


def lin(x, y, args=None):
    return (x * y.T)[0, 0]


def lap(x, y, sigma):
    return exp(-sigma * linalg.norm(x - y))


def gauss(x, y, sigma):
    return exp(-sigma ** 2 * linalg.norm(x - y) ** 2)


def poly(x, y, d):
    a = 1.0
    return (a * (x * y.T)[0, 0]) ** d


def sigmoid(x, y, a):
    c = 0
    return tanh(a * (x * y.T)[0, 0] + c)


def comb_sum(x, y, sigma):
    return lin(x, y) + gauss(x, y, sigma)


def comb_poly(x, y, sig):
    return poly(x, y, 3) + gauss(x, y, sig)


def comb_wsum(x, y, a):
    return a ** 2 * lin(x, y) + (1 - a) ** 2 * gauss(x, y)


def comb_prod(x, y, args):
    return lin(x, y) * gauss(x, y, args)


class SVM:

    def __init__(self, X, Y, error):
        self.X = X
        self.Y = Y
        self.a = None
        self.e = error
        self.predict = None

    def sample(self):
        X = self.X
        Y = self.Y
        XY = zip(X, Y)
        smpl = [XY[i] for i in sample(xrange(len(XY)), 200)]
        self.X, self.Y = zip(*smpl)

    def solve(self, kern=lin, args=None):
        X = self.X
        Y = self.Y
        e = self.e
        X = matrix(X, dtype='float')
        Y = matrix(Y, dtype='float').T
        C = 1
        N = len(X)
        E = ones((N, 1))
        E2 = vstack((E, E))
        K = matrix([[kern(x, y, args) for x in X] for y in X])
        P = vstack((hstack((K, -K)), hstack((-K, K))))
        q = vstack((e * E - Y, e * E + Y))
        A = vstack((E, -E)).T
        I = eye(2 * N)
        G = vstack((-I, I))
        h = vstack((0 * E2, C * E2))
        b = 0.0
        sol = QP(m(P), m(q), G=m(G), h=m(h), A=m(A), b=m(b))
        self.a = matrix(sol['x'])
        st = sol['status']

        def predict(x):
            b = mean(Y - e - self.a.T * hstack((K, -K)).T)
            k = matrix([[kern(x, y, args)] for y in X])
            return self.a.T * vstack((k, -k)) + b
        self.predict = predict
        return st


<mask token>

<mask token>


def lin(x, y, args=None):
    return (x * y.T)[0, 0]


def lap(x, y, sigma):
    return exp(-sigma * linalg.norm(x - y))


def gauss(x, y, sigma):
    return exp(-sigma ** 2 * linalg.norm(x - y) ** 2)


def poly(x, y, d):
    a = 1.0
    return (a * (x * y.T)[0, 0]) ** d


def sigmoid(x, y, a):
    c = 0
    return tanh(a * (x * y.T)[0, 0] + c)


def comb_sum(x, y, sigma):
    return lin(x, y) + gauss(x, y, sigma)


def comb_poly(x, y, sig):
    return poly(x, y, 3) + gauss(x, y, sig)


def comb_wsum(x, y, a):
    return a ** 2 * lin(x, y) + (1 - a) ** 2 * gauss(x, y)


def comb_prod(x, y, args):
    return lin(x, y) * gauss(x, y, args)


class SVM:

    def __init__(self, X, Y, error):
        self.X = X
        self.Y = Y
        self.a = None
        self.e = error
        self.predict = None

    def sample(self):
        X = self.X
        Y = self.Y
        XY = zip(X, Y)
        smpl = [XY[i] for i in sample(xrange(len(XY)), 200)]
        self.X, self.Y = zip(*smpl)

    def solve(self, kern=lin, args=None):
        X = self.X
        Y = self.Y
        e = self.e
        X = matrix(X, dtype='float')
        Y = matrix(Y, dtype='float').T
        C = 1
        N = len(X)
        E = ones((N, 1))
        E2 = vstack((E, E))
        K = matrix([[kern(x, y, args) for x in X] for y in X])
        P = vstack((hstack((K, -K)), hstack((-K, K))))
        q = vstack((e * E - Y, e * E + Y))
        A = vstack((E, -E)).T
        I = eye(2 * N)
        G = vstack((-I, I))
        h = vstack((0 * E2, C * E2))
        b = 0.0
        sol = QP(m(P), m(q), G=m(G), h=m(h), A=m(A), b=m(b))
        self.a = matrix(sol['x'])
        st = sol['status']

        def predict(x):
            b = mean(Y - e - self.a.T * hstack((K, -K)).T)
            k = matrix([[kern(x, y, args)] for y in X])
            return self.a.T * vstack((k, -k)) + b
        self.predict = predict
        return st


if __name__ == '__main__':
    args = []
    for term in sys.argv[1:len(sys.argv)]:
        args.append(unicode(term))
    l = LFSC(args)
    solve(l)

from cvxopt.solvers import qp as QP
from random import sample
from cvxopt import matrix as m
from numpy import tanh, exp, mean, vstack, hstack, ones, zeros, matrix, random, linalg, arange, eye
import numpy.linalg as linalg

def lin(x,y,args=None):
    return (x*y.T)[0,0]

def lap(x,y,sigma):
    return exp(-sigma*linalg.norm(x-y))

def gauss(x,y,sigma):
    return exp(-sigma**2*linalg.norm(x-y)**2)

def poly(x,y,d):
    a = 1.0
    return (a*(x*y.T)[0,0])**d

def sigmoid(x,y,a):
    c =0
    return tanh(a*((x*y.T)[0,0])+c)

def comb_sum(x,y,sigma):
    return lin(x,y) + gauss(x,y,sigma)

def comb_poly(x,y,sig):
    return poly(x,y,3)+gauss(x,y,sig)

def comb_wsum(x,y,a):
    return (a**2)*lin(x,y)+(1-a)**2*gauss(x,y)

def comb_prod(x,y,args):
    return lin(x,y)*gauss(x,y, args)

class SVM:

    def __init__(self,X,Y,error):
        self.X = X
        self.Y = Y
        self.a = None
        self.e = error
        self.predict = None

    def sample(self):
        X = self.X
        Y = self.Y
        XY = zip(X,Y)
        smpl = [XY[i] for i in sample(xrange(len(XY)),200)]
        self.X, self.Y =zip(*smpl)

    def solve(self, kern=lin, args=None):
        X = self.X
        Y = self.Y
        e = self.e
       # OBX = matrix([[0.0008,3.0]
       #          ,[2,20.0]
       #          ,[0.0004,1]
       #          ,[5,0]])
       # Y = matrix([0.0,2.0,1.0,3.0]).T
       # X = matrix(data.XY[0],dtype='float')
       # Y = matrix(data.XY[1],dtype='float').T
        #trim the datapoints before passing to svm
        X = matrix(X, dtype='float')
        Y = matrix(Y, dtype='float').T
        C = 1
        N = len(X)
        E = ones((N,1))
        E2 = vstack((E, E))
        K = matrix([[kern(x,y,args) for x in X] for y in X])
        #K = X*X.T
        P = vstack((hstack(( K,-K))
                  , hstack((-K, K))))
        #a trick to increase the rank of P without perturbing data too much
        #P = P + np.eye(2*N)*1e-10
        q = vstack((e*E-Y, e*E+Y))

        A = vstack((E, -E)).T
        I = eye(2*N)
        G = vstack((-I, I))
        h = vstack((0*E2,C*E2))
        b = 0.0
        sol = QP(m(P), m(q), G=m(G), h=m(h), A=m(A), b =m(b))
        self.a = matrix(sol['x'])
        st = sol['status']


        def predict (x):
            b = mean(Y - e - self.a.T * hstack((K,-K)).T)
            k = matrix([[kern(x,y,args)] for y in X])
            return self.a.T * vstack((k,-k)) + b

        self.predict = predict
        return st


if __name__ == "__main__":
    args = []
    for term in sys.argv[1:len(sys.argv)]:
        args.append(unicode(term))
    l = LFSC(args)
    solve(l)
