def norm2( x ):
"""
Compute the 2-norm of a vector, returning alpha
x can be a row or column vector.
"""
assert type(x) is np.matrix and len(x.shape) is 2, \
"laff.norm2: vector x must be a 2D numpy.matrix"
m, n = np.shape(x)
assert m is 1 or n is 1, \
"laff.norm2: x is not a vector"
#Ensure that we don't modify x in
#any way by copying it to a new vector, y
y = np.matrix( np.zeros( (m,n) ) )
laff.copy( x, y )
#Initialize variables that we will use to appropriate values
alpha = 0
maxval = y[ 0, 0 ]
if m is 1: #y is a row
#Find a value to scale by to avoid under/overflow
for i in range(n):
if abs(y[ 0, i ]) > maxval:
maxval = abs(y[ 0, i ])
elif n is 1: #y is a column
#Find a value to scale by to avoid under/overflow
for i in range(m):
if abs(y[ i, 0 ]) > maxval:
maxval = abs(y[ i, 0 ])
#If y is the zero vector, return 0
if abs(maxval) < 1e-7:
return 0
#Scale all of the values by 1/maxval to prevent under/overflow
laff.scal( 1.0/maxval, y )
alpha = maxval * sqrt( laff.dot( y, y ) )
return alpha
def norm2( x ):
"""
Compute the 2-norm of a vector, returning alpha
x can be a row or column vector.
"""
assert type(x) is np.matrix and len(x.shape) is 2, \
"laff.norm2: vector x must be a 2D numpy.matrix"
m, n = np.shape(x)
assert m is 1 or n is 1, \
"laff.norm2: x is not a vector"
#Ensure that we don't modify x in
#any way by copying it to a new vector, y
y = np.matrix( np.zeros( (m,n) ) )
laff.copy( x, y )
#Initialize variables that we will use to appropriate values
alpha = 0
maxval = y[ 0, 0 ]
if m is 1: #y is a row
#Find a value to scale by to avoid under/overflow
for i in range(n):
if abs(y[ 0, i ]) > maxval:
maxval = y[ 0, i ]
elif n is 1: #y is a column
#Find a value to scale by to avoid under/overflow
for i in range(m):
if abs(y[ i, 0 ]) > maxval:
maxval = y[ i, 0 ]
#If y is the zero vector, return 0
if maxval is 0:
return 0
#Scale all of the values by 1/maxval to prevent under/overflow
laff.scal( 1.0/maxval, y )
alpha = maxval * sqrt( laff.dot( y, y ) )
return alpha
def norm2(x):
"""
"""
assert type(x) is np.matrix and len(
x.shape) is 2, "laff.norm2: vector x must be a 2D numpy.matrix"
m, n = np.shape(x)
assert m is 1 or n is 1, "laff.norm2: x is not a vector"
y = np.matrix(np.zeros((m, n)))
laff.copy(x, y)
alpha = 0
maxval = y[0, 0]
if m is 1: #y is a row
for i in range(n):
if abs(y[0, i]) > maxval:
maxval = abs(y[0, i])
elif n is 1: #y is a column
for i in range(m):
if abs(y[i, 0]) > maxval:
maxval = abs(y[i, 0])
if abs(maxval) < 1e-7:
return 0
laff.scal(1.0 / maxval, y)
alpha = maxval * sqrt(laff.dot(y, y))
return alpha
def norm2( x ):
"""
"""
assert type(x) is np.matrix and len(x.shape) is 2, "laff.norm2: vector x must be a 2D numpy.matrix"
m, n = np.shape(x)
assert m is 1 or n is 1, "laff.norm2: x is not a vector"
y = np.matrix( np.zeros( (m,n) ) )
laff.copy( x, y )
alpha = 0
maxval = y[ 0, 0 ]
if m is 1: #y is a row
for i in range(n):
if abs(y[ 0, i ]) > maxval:
maxval = abs(y[ 0, i ])
elif n is 1: #y is a column
for i in range(m):
if abs(y[ i, 0 ]) > maxval:
maxval = abs(y[ i, 0 ])
if abs(maxval) < 1e-7:
return 0
laff.scal( 1.0/maxval, y )
alpha = maxval * sqrt( laff.dot( y, y ) )
return alpha
