def test_dot_returns_maskedarray(self):
# See gh-6611
a = np.eye(3)
b = array(a)
assert_(type(dot(a, a)) is MaskedArray)
assert_(type(dot(a, b)) is MaskedArray)
assert_(type(dot(b, a)) is MaskedArray)
assert_(type(dot(b, b)) is MaskedArray)
def test_dot_returns_maskedarray(self):
# See gh-6611
a = np.eye(3)
b = array(a)
assert_(type(dot(a, a)) is MaskedArray)
assert_(type(dot(a, b)) is MaskedArray)
assert_(type(dot(b, a)) is MaskedArray)
assert_(type(dot(b, b)) is MaskedArray)
def gmmEM(data, K, it,show=False,usekmeans=True):
#data += finfo(float128).eps*100
centroid = kmeans2(data, K)[0] if usekmeans else ((max(data) - min(data))*random_sample((K,data.shape[1])) + min(data))
N = data.shape[0]
gmm = GaussianMM(centroid)
if show: gmm.draw(data)
while it > 0:
print it," iterations remaining"
it = it - 1
# e-step
gausses = zeros((K, N), dtype = data.dtype)
for k in range(0, K):
gausses[k] = gmm.c[k]*mulnormpdf(data, gmm.mean[k], gmm.covm[k])
sums = sum(gausses, axis=0)
if count_nonzero(sums) != sums.size:
raise "Divide by Zero"
gausses /= sums
# m step
sg = sum(gausses, axis=1)
if count_nonzero(sg) != sg.size:
raise "Divide by Zero"
gmm.c = ones(sg.shape) / N * sg
for k in range(0, K):
gmm.mean[k] = sum(data * gausses[k].reshape((-1,1)), axis=0) / sg[k]
d = data - gmm.mean[k]
d1 = d.transpose()*gausses[k]
gmm.covm[k]=dot(d1,d)/sg[k]
if show: gmm.draw(data)
return gmm
def train(self, data, labels):
l = labels.reshape((-1,1))
xy = data * l
H = dot(xy,transpose(xy))
f = -1.0*ones(labels.shape)
lb = zeros(labels.shape)
ub = self.C * ones(labels.shape)
Aeq = labels
beq = 0.0
p = QP(matrix(H),f.tolist(),lb=lb.tolist(),ub=ub.tolist(),Aeq=Aeq.tolist(),beq=beq)
r = p.solve('cvxopt_qp')
r.xf[where(r.xf<1e-3)] = 0
self.w = dot(r.xf*labels,data)
nonzeroindexes = where(r.xf>1e-4)[0]
l1 = nonzeroindexes[0]
self.w0 = 1.0/labels[l1]-dot(self.w,data[l1])
self.numSupportVectors = len(nonzeroindexes)
def test_matrix_product(self):
A = array( [[1,1],
[0,1]] )
B = array( [[2,0],
[3,4]] )
C = dot(A,B)
numpy.testing.assert_array_equal(C,array([[5, 4],
[3, 4]]))
def __train__(self, data, labels):
l = labels.reshape((-1,1))
xy = data * l
H = dot(xy,transpose(xy))
f = -1.0*ones(labels.shape)
lb = zeros(labels.shape)
ub = self.C * ones(labels.shape)
Aeq = labels
beq = 0.0
devnull = open('/dev/null', 'w')
oldstdout_fno = os.dup(sys.stdout.fileno())
os.dup2(devnull.fileno(), 1)
p = QP(matrix(H),f.tolist(),lb=lb.tolist(),ub=ub.tolist(),Aeq=Aeq.tolist(),beq=beq)
r = p.solve('cvxopt_qp')
os.dup2(oldstdout_fno, 1)
lim = 1e-4
r.xf[where(r.xf<lim)] = 0
self.w = dot(r.xf*labels,data)
nonzeroindexes = where(r.xf>lim)[0]
l1 = nonzeroindexes[0]
self.w0 = 1.0/labels[l1]-dot(self.w,data[l1])
self.numSupportVectors = len(nonzeroindexes)
def test_simple_array_operations(self):
a = array([[1.,2.],
[3.,4.]])
numpy.testing.assert_array_equal(a.transpose(), array([[1.,3.],
[2.,4.]]))
numpy.testing.assert_array_almost_equal(trace(a), 5)
inv_a = inv(a)
b = array([[-2.,1.],
[1.5,-.5]])
self.assertTrue(numpy.allclose(inv_a,b))
i = dot(a,inv_a)
numpy.testing.assert_array_almost_equal(i, eye(2), 1)
numpy.testing.assert_array_almost_equal(inv_a, b)
# system of linear equations
a = array([[3,2,-1],
[2,-2,4],
[-1,0.5,-1]])
b = array([1,-2,0])
c = solve(a,b)
d = dot(a,c)
numpy.testing.assert_array_almost_equal(b, d, 1)
a = array([[.8,.3],
[.2,.7]])
eigen_values, eigen_vectors = eig(a)
lambda_1 = eigen_values[0]
x_1 = eigen_vectors[:,0]
lambda_2 = eigen_values[1]
x_2 = eigen_vectors[:,1]
def test_dot(self):
# Tests dot product
n = np.arange(1, 7)
#
m = [1, 0, 0, 0, 0, 0]
a = masked_array(n, mask=m).reshape(2, 3)
b = masked_array(n, mask=m).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[1, 1], [1, 0]])
c = dot(b, a, strict=True)
assert_equal(c.mask, [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
m = [0, 0, 0, 0, 0, 1]
a = masked_array(n, mask=m).reshape(2, 3)
b = masked_array(n, mask=m).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[0, 1], [1, 1]])
c = dot(b, a, strict=True)
assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [1, 1, 1]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
assert_equal(c, dot(a, b))
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
m = [0, 0, 0, 0, 0, 0]
a = masked_array(n, mask=m).reshape(2, 3)
b = masked_array(n, mask=m).reshape(3, 2)
c = dot(a, b)
assert_equal(c.mask, nomask)
c = dot(b, a)
assert_equal(c.mask, nomask)
#
a = masked_array(n, mask=[1, 0, 0, 0, 0, 0]).reshape(2, 3)
b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[1, 1], [0, 0]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=True)
assert_equal(c.mask, [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[0, 0], [1, 1]])
c = dot(a, b)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=True)
assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [0, 0, 1]])
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
b = masked_array(n, mask=[0, 0, 1, 0, 0, 0]).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[1, 0], [1, 1]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=True)
assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]])
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
def test_dot_out(self):
a = array(np.eye(3))
out = array(np.zeros((3, 3)))
res = dot(a, a, out=out)
assert_(res is out)
assert_equal(a, res)
def test_dot(self):
# Tests dot product
n = np.arange(1, 7)
#
m = [1, 0, 0, 0, 0, 0]
a = masked_array(n, mask=m).reshape(2, 3)
b = masked_array(n, mask=m).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[1, 1], [1, 0]])
c = dot(b, a, strict=True)
assert_equal(c.mask, [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
m = [0, 0, 0, 0, 0, 1]
a = masked_array(n, mask=m).reshape(2, 3)
b = masked_array(n, mask=m).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[0, 1], [1, 1]])
c = dot(b, a, strict=True)
assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [1, 1, 1]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
assert_equal(c, dot(a, b))
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
m = [0, 0, 0, 0, 0, 0]
a = masked_array(n, mask=m).reshape(2, 3)
b = masked_array(n, mask=m).reshape(3, 2)
c = dot(a, b)
assert_equal(c.mask, nomask)
c = dot(b, a)
assert_equal(c.mask, nomask)
#
a = masked_array(n, mask=[1, 0, 0, 0, 0, 0]).reshape(2, 3)
b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[1, 1], [0, 0]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=True)
assert_equal(c.mask, [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[0, 0], [1, 1]])
c = dot(a, b)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=True)
assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [0, 0, 1]])
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
#
a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
b = masked_array(n, mask=[0, 0, 1, 0, 0, 0]).reshape(3, 2)
c = dot(a, b, strict=True)
assert_equal(c.mask, [[1, 0], [1, 1]])
c = dot(a, b, strict=False)
assert_equal(c, np.dot(a.filled(0), b.filled(0)))
c = dot(b, a, strict=True)
assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]])
c = dot(b, a, strict=False)
assert_equal(c, np.dot(b.filled(0), a.filled(0)))
def __Mstep__(self):
for k in range(0,self.K):
self.c[k,:] = 1.0/self.N * sum(self.p,axis=1)
self.mean[k,:] = sum(self.p*self.data, axis=1)/sum(self.p,axis=1)
self.covm[k,:,:] = sum(self.p*dot(self.data - self.mean[k,:],transpose(self.data - self.mean[k,:])),axis=1)/sum(self.p,axis=1)
def classify(self,data,labels):
res = dot(data,self.w)
return getRootMeanSquaredErrors(res, labels)
'''
Created on Sep 23, 2012
@author: will
'''
from numpy.core.numeric import arange, outer
from numpy.ma.extras import dot
if __name__ == '__main__':
x = arange(10)
print x
print dot(x,x)
print outer(x,x)
def __classify__(self, data):
t = dot(data,self.w.reshape((-1,1))) + self.w0
return t > 0
def __train__(self, data, labels):
o=ones((data.shape[0],1))
h=hstack((data, o))
pseudoX=pinv(h)
self.w=dot(pseudoX, labels.reshape((-1,1)))
def __classify__(self, data):
t=dot(hstack((data, ones((data.shape[0],1)))),self.w)
return t>0
def test_dot_out(self):
a = array(np.eye(3))
out = array(np.zeros((3, 3)))
res = dot(a, a, out=out)
assert_(res is out)
assert_equal(a, res)
def train(self,data,labels):
self.w = dot(pinv(data.newbyteorder('=')),labels)
self.trainingError = self.classify(data, labels)
