def stdForm(a, b):
def invert(L): # Inverts lower triangular matrix L
n = len(L)
for j in range(n - 1):
L[j, j] = 1.0 / L[j, j]
for i in range(j + 1, n):
L[i, j] = -dot(L[i, j:i], L[j:i, j]) / L[i, i]
L[n - 1, n - 1] = 1.0 / L[n - 1, n - 1]
n = len(a)
L = choleski(b)
invert(L)
h = matrixmultiply(b, matrixmultiply(a, transpose(L)))
return h, transpose(L)
def stdForm(a, b):
def invert(L): # Inverts lower triangular matrix L
n = len(L)
for j in range(n - 1):
L[j, j] = 1.0 / L[j, j]
for i in range(j + 1, n):
L[i, j] = -dot(L[i, j:i], L[j:i, j]) / L[i, i]
L[n - 1, n - 1] = 1.0 / L[n - 1, n - 1]
n = len(a)
L = choleski(b)
invert(L)
h = matrixmultiply(b, matrixmultiply(a, transpose(L)))
return h, transpose(L)
def triangleQuad(f, xc, yc):
alpha = array([[1.0/3, 1.0/3.0, 1.0/3.0], \
[0.2, 0.2, 0.6], \
[0.6, 0.2, 0.2], \
[0.2, 0.6, 0.2]])
W = array([-27.0 / 48.0, 25.0 / 48.0, 25.0 / 48.0, 25.0 / 48.0])
x = matrixmultiply(alpha, xc)
y = matrixmultiply(alpha, yc)
A = (xc[1]*yc[2] - xc[2]*yc[1] \
- xc[0]*yc[2] + xc[2]*yc[0] \
+ xc[0]*yc[1] - xc[1]*yc[0])/2.0
sum = 0.0
for i in range(4):
sum = sum + W[i] * f(x[i], y[i])
return A * sum
def triangleQuad(f,xc,yc):
alpha = array([[1.0/3, 1.0/3.0, 1.0/3.0], \
[0.2, 0.2, 0.6], \
[0.6, 0.2, 0.2], \
[0.2, 0.6, 0.2]])
W = array([-27.0/48.0 ,25.0/48.0, 25.0/48.0, 25.0/48.0])
x = matrixmultiply(alpha,xc)
y = matrixmultiply(alpha,yc)
A = (xc[1]*yc[2] - xc[2]*yc[1] \
- xc[0]*yc[2] + xc[2]*yc[0] \
+ xc[0]*yc[1] - xc[1]*yc[0])/2.0
sum = 0.0
for i in range(4):
sum = sum + W[i] * f(x[i],y[i])
return A*sum
def gen_rotations(theta1, theta2, theta3): global rot_matrix theta1, theta2, theta3 = radians(theta1), radians(theta2), radians(theta3) rotate_x = array(sequence=(1, 0, 0, 0, cos(theta1), -sin(theta1), 0, sin(theta1), cos(theta1)),shape=(3,3)) rotate_y = array(sequence=(cos(theta2),0, -sin(theta2), 0, 1, 0, sin(theta2),0, cos(theta2)),shape=(3,3)) rotate_z = array(sequence=(cos(theta3), -sin(theta3), 0, sin(theta3), cos(theta3), 0, 0, 0, 1,),shape=(3,3)) rot_matrix = matrixmultiply(matrixmultiply(rotate_z,rotate_y),rotate_x)
def _gaussian(self, mean, cvm, x):
m = len(mean)
assert cvm.shape == (m, m), \
'bad sized covariance matrix, %s' % str(cvm.shape)
try:
det = numarray.linear_algebra.determinant(cvm)
inv = numarray.linear_algebra.inverse(cvm)
a = det ** -0.5 * (2 * numarray.pi) ** (-m / 2.0)
dx = x - mean
b = -0.5 * numarray.matrixmultiply( \
numarray.matrixmultiply(dx, inv), dx)
return a * numarray.exp(b)
except OverflowError:
# happens when the exponent is negative infinity - i.e. b = 0
# i.e. the inverse of cvm is huge (cvm is almost zero)
return 0
def cluster(self, tokens, assign_clusters=False, trace=False):
assert chktype(1, tokens, [Token])
assert chktype(2, assign_clusters, bool)
assert chktype(3, trace, bool)
assert len(tokens) > 0
vectors = map(lambda tk: tk['FEATURES'], tokens)
# normalise the vectors
if self._should_normalise:
vectors = map(self._normalise, vectors)
# use SVD to reduce the dimensionality
if self._svd_dimensions and self._svd_dimensions < len(vectors[0]):
[u, d, vt] = numarray.linear_algebra.singular_value_decomposition(
numarray.transpose(numarray.array(vectors)))
S = d[:self._svd_dimensions] * \
numarray.identity(self._svd_dimensions, numarray.Float64)
T = u[:,:self._svd_dimensions]
Dt = vt[:self._svd_dimensions,:]
vectors = numarray.transpose(numarray.matrixmultiply(S, Dt))
self._Tt = numarray.transpose(T)
# call abstract method to cluster the vectors
self.cluster_vectorspace(vectors, trace)
# assign the tokens to clusters
if assign_clusters:
for token in tokens:
self.classify(token)
def getWeights(sample):
# Evaluate PDFs for this sample.
pds = numarray.array([ f(sample) for f in pdfs ])
# Compute an array of the weights.
denominator = numarray.dot(nums, pds)
if denominator != 0:
return numarray.matrixmultiply(V, pds) / denominator
else:
return numarray.zeros((num_categories, ), "Float32")
def computeP(a):
n = len(a)
p = identity(n)*1.0
for k in range(n-2):
u = a[k+1:n,k]
h = dot(u,u)/2.0
v = matrixmultiply(p[1:n,k+1:n],u)/h
p[1:n,k+1:n] = p[1:n,k+1:n] - outerproduct(v,u)
return p
def likelihood(self, labelled_token):
assert chktype(1, labelled_token, Token)
vector = labelled_token['FEATURES']
#assert chktype('features', vector, numarray.array([]), SparseArray)
if self._should_normalise:
vector = self._normalise(vector)
if self._Tt != None:
vector = numarray.matrixmultiply(self._Tt, vector)
return self.likelihood_vectorspace(vector, labelled_token['CLUSTER'])
def computeP(a):
n = len(a)
p = identity(n) * 1.0
for k in range(n - 2):
u = a[k + 1:n, k]
h = dot(u, u) / 2.0
v = matrixmultiply(p[1:n, k + 1:n], u) / h
p[1:n, k + 1:n] = p[1:n, k + 1:n] - outerproduct(v, u)
return p
def classify(self, token):
assert chktype(1, token, Token)
vector = token['FEATURES']
#assert chktype('features', vector, numarray.array([]), SparseArray)
if self._should_normalise:
vector = self._normalise(vector)
if self._Tt != None:
vector = numarray.matrixmultiply(self._Tt, vector)
cluster = self.classify_vectorspace(vector)
token['CLUSTER'] = self.cluster_name(cluster)
def vector(self, token):
"""
Returns the vector after normalisation and dimensionality reduction
for the given token's FEATURES.
"""
assert chktype(1, token, Token)
vector = token['FEATURES']
#assert chktype('features', vector, numarray.array([]), SparseArray)
if self._should_normalise:
vector = self._normalise(vector)
if self._Tt != None:
vector = numarray.matrixmultiply(self._Tt, vector)
return vector
def householder(a):
n = len(a)
for k in range(n-2):
u = a[k+1:n,k]
uMag = sqrt(dot(u,u))
if u[0] < 0.0: uMag = -uMag
u[0] = u[0] + uMag
h = dot(u,u)/2.0
v = matrixmultiply(a[k+1:n,k+1:n],u)/h
g = dot(u,v)/(2.0*h)
v = v - g*u
a[k+1:n,k+1:n] = a[k+1:n,k+1:n] - outerproduct(v,u) \
- outerproduct(u,v)
a[k,k+1] = -uMag
return diagonal(a),diagonal(a,1)
def householder(a):
n = len(a)
for k in range(n - 2):
u = a[k + 1:n, k]
uMag = sqrt(dot(u, u))
if u[0] < 0.0: uMag = -uMag
u[0] = u[0] + uMag
h = dot(u, u) / 2.0
v = matrixmultiply(a[k + 1:n, k + 1:n], u) / h
g = dot(u, v) / (2.0 * h)
v = v - g * u
a[k+1:n,k+1:n] = a[k+1:n,k+1:n] - outerproduct(v,u) \
- outerproduct(u,v)
a[k, k + 1] = -uMag
return diagonal(a), diagonal(a, 1)
def rotate(input,
angle,
axes=(-1, -2),
reshape=True,
output_type=None,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True):
"""Rotate an array.
The array is rotated in the plane defined by the two axes given by the
axes parameter using spline interpolation of the requested order. The
angle is given in degrees. Points outside the boundaries of the input
are filled according to the given mode. If reshape is true, the output
shape is adapted so that the input array is contained completely in
the output. The parameter prefilter determines if the input is pre-
filtered before interpolation, if False it is assumed that the input
is already filtered.
"""
input = numarray.asarray(input)
axes = list(axes)
rank = input.rank
if axes[0] < 0:
axes[0] += rank
if axes[1] < 0:
axes[1] += rank
if axes[0] < 0 or axes[1] < 0 or axes[0] > rank or axes[1] > rank:
raise RuntimeError, 'invalid rotation plane specified'
if axes[0] > axes[1]:
axes = axes[1], axes[0]
angle = numarray.pi / 180 * angle
m11 = math.cos(angle)
m12 = math.sin(angle)
m21 = -math.sin(angle)
m22 = math.cos(angle)
matrix = numarray.array([[m11, m12], [m21, m22]], type=numarray.Float64)
iy = input.shape[axes[0]]
ix = input.shape[axes[1]]
if reshape:
mtrx = numarray.array([[m11, -m21], [-m12, m22]],
type=numarray.Float64)
minc = [0, 0]
maxc = [0, 0]
coor = numarray.matrixmultiply(mtrx, [0, ix])
minc, maxc = _minmax(coor, minc, maxc)
coor = numarray.matrixmultiply(mtrx, [iy, 0])
minc, maxc = _minmax(coor, minc, maxc)
coor = numarray.matrixmultiply(mtrx, [iy, ix])
minc, maxc = _minmax(coor, minc, maxc)
oy = int(maxc[0] - minc[0] + 0.5)
ox = int(maxc[1] - minc[1] + 0.5)
else:
oy = input.shape[axes[0]]
ox = input.shape[axes[1]]
offset = numarray.zeros((2, ), type=numarray.Float64)
offset[0] = float(oy) / 2.0 - 0.5
offset[1] = float(ox) / 2.0 - 0.5
offset = numarray.matrixmultiply(matrix, offset)
tmp = numarray.zeros((2, ), type=numarray.Float64)
tmp[0] = float(iy) / 2.0 - 0.5
tmp[1] = float(ix) / 2.0 - 0.5
offset = tmp - offset
output_shape = list(input.shape)
output_shape[axes[0]] = oy
output_shape[axes[1]] = ox
output_shape = tuple(output_shape)
output, return_value = _ni_support._get_output(output,
input,
output_type,
shape=output_shape)
if input.rank <= 2:
affine_transform(input, matrix, offset, output_shape, None, output,
order, mode, cval, prefilter)
else:
coordinates = []
size = input.nelements()
size /= input.shape[axes[0]]
size /= input.shape[axes[1]]
for ii in range(input.rank):
if ii not in axes:
coordinates.append(0)
else:
coordinates.append(slice(None, None, None))
iter_axes = range(input.rank)
iter_axes.reverse()
iter_axes.remove(axes[0])
iter_axes.remove(axes[1])
os = (output_shape[axes[0]], output_shape[axes[1]])
for ii in range(size):
ia = input[tuple(coordinates)]
oa = output[tuple(coordinates)]
affine_transform(ia, matrix, offset, os, None, oa, order, mode,
cval, prefilter)
for jj in iter_axes:
if coordinates[jj] < input.shape[jj] - 1:
coordinates[jj] += 1
break
else:
coordinates[jj] = 0
return return_value
## example9_14
from householder import *
from eigenvals3 import *
from inversePower3 import *
from numarray import array,zeros,Float64,matrixmultiply
N = 3 # Number of eigenvalues requested
a = array([[ 11.0, 2.0, 3.0, 1.0, 4.0], \
[ 2.0, 9.0, 3.0, 5.0, 2.0], \
[ 3.0, 3.0, 15.0, 4.0, 3.0], \
[ 1.0, 5.0, 4.0, 12.0, 4.0], \
[ 4.0, 2.0, 3.0, 4.0, 17.0]])
xx = zeros((len(a),N),type=Float64)
d,c = householder(a) # Tridiagonalize [A]
p = computeP(a) # Compute transformation matrix
lambdas = eigenvals3(d,c,N) # Compute eigenvalues
for i in range(N):
s = lambdas[i]*1.0000001 # Shift very close to eigenvalue
lam,x = inversePower3(d,c,s) # Compute eigenvector [x]
xx[:,i] = x # Place [x] in array [xx]
xx = matrixmultiply(p,xx) # Recover eigenvectors of [A]
print "Eigenvalues:\n",lambdas
print "\nEigenvectors:\n",xx
raw_input("Press return to exit")
def scale_point(point, factor): t_scale = array(sequence=(factor, 0, 0, 0, factor, 0, 0, 0, factor), shape=(3,3)) return matrixmultiply(t_scale, array(sequence=point, shape=(3,1))).tolist()
def transform(self, t): tmp = transpose(array(sequence=self.points, shape=(len(self.points), 3))) self.points = transpose(matrixmultiply(t, tmp)).tolist()
def transformPoints(self, vector): b = matrixmultiply(self.T, vector) return b
## example9_14
from householder import *
from eigenvals3 import *
from inversePower3 import *
from numarray import array, zeros, Float64, matrixmultiply
N = 3 # Number of eigenvalues requested
a = array([[ 11.0, 2.0, 3.0, 1.0, 4.0], \
[ 2.0, 9.0, 3.0, 5.0, 2.0], \
[ 3.0, 3.0, 15.0, 4.0, 3.0], \
[ 1.0, 5.0, 4.0, 12.0, 4.0], \
[ 4.0, 2.0, 3.0, 4.0, 17.0]])
xx = zeros((len(a), N), type=Float64)
d, c = householder(a) # Tridiagonalize [A]
p = computeP(a) # Compute transformation matrix
lambdas = eigenvals3(d, c, N) # Compute eigenvalues
for i in range(N):
s = lambdas[i] * 1.0000001 # Shift very close to eigenvalue
lam, x = inversePower3(d, c, s) # Compute eigenvector [x]
xx[:, i] = x # Place [x] in array [xx]
xx = matrixmultiply(p, xx) # Recover eigenvectors of [A]
print "Eigenvalues:\n", lambdas
print "\nEigenvectors:\n", xx
raw_input("Press return to exit")
#!/usr/bin/python
## example2_8
from numarray import array,matrixmultiply,transpose
from choleski import *
a = array([[ 1.44, -0.36, 5.52, 0.0], \
[-0.36, 10.33, -7.78, 0.0], \
[ 5.52, -7.78, 28.40, 9.0], \
[ 0.0, 0.0, 9.0, 61.0]])
L = choleski(a)
print 'L =\n',L
print '\nCheck: L*L_transpose =\n', \
matrixmultiply(L,transpose(L))
raw_input("\nPress return to exit")
## example9_4
from numarray import array, matrixmultiply, dot
from math import sqrt
s = array([[-30.0, 10.0, 20.0], \
[ 10.0, 40.0, -50.0], \
[ 20.0, -50.0, -10.0]])
v = array([1.0, 0.0, 0.0])
for i in range(100):
vOld = v.copy()
z = matrixmultiply(s, v)
zMag = sqrt(dot(z, z))
v = z / zMag
if dot(vOld, v) < 0.0:
sign = -1.0
v = -v
else:
sign = 1.0
if sqrt(dot(vOld - v, vOld - v)) < 1.0e-6: break
lam = sign * zMag
print "Number of iterations =", i
print "Eigenvalue =", lam
raw_input("Press return to exit")
#!/usr/bin/python
## example2_8
from numarray import array, matrixmultiply, transpose
from choleski import *
a = array([[ 1.44, -0.36, 5.52, 0.0], \
[-0.36, 10.33, -7.78, 0.0], \
[ 5.52, -7.78, 28.40, 9.0], \
[ 0.0, 0.0, 9.0, 61.0]])
L = choleski(a)
print 'L =\n', L
print '\nCheck: L*L_transpose =\n', \
matrixmultiply(L,transpose(L))
raw_input("\nPress return to exit")
def rotate(input, angle, axes = (-1, -2), reshape = True,
output_type = None, output = None, order = 3,
mode = 'constant', cval = 0.0, prefilter = True):
"""Rotate an array.
The array is rotated in the plane defined by the two axes given by the
axes parameter using spline interpolation of the requested order. The
angle is given in degrees. Points outside the boundaries of the input
are filled according to the given mode. If reshape is true, the output
shape is adapted so that the input array is contained completely in
the output. The parameter prefilter determines if the input is pre-
filtered before interpolation, if False it is assumed that the input
is already filtered.
"""
input = numarray.asarray(input)
axes = list(axes)
rank = input.rank
if axes[0] < 0:
axes[0] += rank
if axes[1] < 0:
axes[1] += rank
if axes[0] < 0 or axes[1] < 0 or axes[0] > rank or axes[1] > rank:
raise RuntimeError, 'invalid rotation plane specified'
if axes[0] > axes[1]:
axes = axes[1], axes[0]
angle = numarray.pi / 180 * angle
m11 = math.cos(angle)
m12 = math.sin(angle)
m21 = -math.sin(angle)
m22 = math.cos(angle)
matrix = numarray.array([[m11, m12],
[m21, m22]], type = numarray.Float64)
iy = input.shape[axes[0]]
ix = input.shape[axes[1]]
if reshape:
mtrx = numarray.array([[ m11, -m21],
[-m12, m22]], type = numarray.Float64)
minc = [0, 0]
maxc = [0, 0]
coor = numarray.matrixmultiply(mtrx, [0, ix])
minc, maxc = _minmax(coor, minc, maxc)
coor = numarray.matrixmultiply(mtrx, [iy, 0])
minc, maxc = _minmax(coor, minc, maxc)
coor = numarray.matrixmultiply(mtrx, [iy, ix])
minc, maxc = _minmax(coor, minc, maxc)
oy = int(maxc[0] - minc[0] + 0.5)
ox = int(maxc[1] - minc[1] + 0.5)
else:
oy = input.shape[axes[0]]
ox = input.shape[axes[1]]
offset = numarray.zeros((2,), type = numarray.Float64)
offset[0] = float(oy) / 2.0 - 0.5
offset[1] = float(ox) / 2.0 - 0.5
offset = numarray.matrixmultiply(matrix, offset)
tmp = numarray.zeros((2,), type = numarray.Float64)
tmp[0] = float(iy) / 2.0 - 0.5
tmp[1] = float(ix) / 2.0 - 0.5
offset = tmp - offset
output_shape = list(input.shape)
output_shape[axes[0]] = oy
output_shape[axes[1]] = ox
output_shape = tuple(output_shape)
output, return_value = _ni_support._get_output(output, input,
output_type, shape = output_shape)
if input.rank <= 2:
affine_transform(input, matrix, offset, output_shape, None, output,
order, mode, cval, prefilter)
else:
coordinates = []
size = input.nelements()
size /= input.shape[axes[0]]
size /= input.shape[axes[1]]
for ii in range(input.rank):
if ii not in axes:
coordinates.append(0)
else:
coordinates.append(slice(None, None, None))
iter_axes = range(input.rank)
iter_axes.reverse()
iter_axes.remove(axes[0])
iter_axes.remove(axes[1])
os = (output_shape[axes[0]], output_shape[axes[1]])
for ii in range(size):
ia = input[tuple(coordinates)]
oa = output[tuple(coordinates)]
affine_transform(ia, matrix, offset, os, None, oa, order, mode,
cval, prefilter)
for jj in iter_axes:
if coordinates[jj] < input.shape[jj] - 1:
coordinates[jj] += 1
break
else:
coordinates[jj] = 0
return return_value
## example9_4
from numarray import array,matrixmultiply,dot
from math import sqrt
s = array([[-30.0, 10.0, 20.0], \
[ 10.0, 40.0, -50.0], \
[ 20.0, -50.0, -10.0]])
v = array([1.0, 0.0, 0.0])
for i in range(100):
vOld = v.copy()
z = matrixmultiply(s,v)
zMag = sqrt(dot(z,z))
v = z/zMag
if dot(vOld,v) < 0.0:
sign = -1.0
v = -v
else: sign = 1.0
if sqrt(dot(vOld - v,vOld - v)) < 1.0e-6: break
lam = sign*zMag
print "Number of iterations =",i
print "Eigenvalue =",lam
raw_input("Press return to exit")
#!/usr/bin/python
## example2_13
from numarray import array,identity, matrixmultiply
from LUpivot import *
def matInv(a):
n = len(a[0])
aInv = identity(n)*1.0
a,seq = LUdecomp(a)
for i in range(n):
aInv[:,i] = LUsolve(a,aInv[:,i],seq)
return aInv
a = array([[ 0.6, -0.4, 1.0],\
[-0.3, 0.2, 0.5],\
[ 0.6, -1.0, 0.5]])
aOrig = a.copy() # Save original [a]
aInv = matInv(a) # Invert [a] (original [a] is destroyed)
print "\naInv =\n",aInv
print "\nCheck: a*aInv =\n", matrixmultiply(aOrig,aInv)
raw_input("\nPress return to exit")
## example2_4
from numarray import zeros,Float64,array,product, \
diagonal,matrixmultiply
from gaussElimin import *
def vandermode(v):
n = len(v)
a = zeros((n, n), type=Float64)
for j in range(n):
a[:, j] = v**(n - j - 1)
return a
v = array([1.0, 1.2, 1.4, 1.6, 1.8, 2.0])
b = array([0.0, 1.0, 0.0, 1.0, 0.0, 1.0])
a = vandermode(v)
aOrig = a.copy() # Save original matrix
bOrig = b.copy() # and the constant vector
x = gaussElimin(a, b)
det = product(diagonal(a))
print 'x =\n', x
print '\ndet =', det
print '\nCheck result: [a]{x} - b =\n', \
matrixmultiply(aOrig,x) - bOrig
raw_input("\nPress return to exit")
## example2_4
from numarray import zeros,Float64,array,product, \
diagonal,matrixmultiply
from gaussElimin import *
def vandermode(v):
n = len(v)
a = zeros((n,n),type=Float64)
for j in range(n):
a[:,j] = v**(n-j-1)
return a
v = array([1.0, 1.2, 1.4, 1.6, 1.8, 2.0])
b = array([0.0, 1.0, 0.0, 1.0, 0.0, 1.0])
a = vandermode(v)
aOrig = a.copy() # Save original matrix
bOrig = b.copy() # and the constant vector
x = gaussElimin(a,b)
det = product(diagonal(a))
print 'x =\n',x
print '\ndet =',det
print '\nCheck result: [a]{x} - b =\n', \
matrixmultiply(aOrig,x) - bOrig
raw_input("\nPress return to exit")
