def fillcontinents(self,ax,color=0.8):
"""
Fill continents.
ax - current axis instance.
color - color to fill continents (default gray).
"""
# define corners of map domain.
p1 = (self.llcrnrx,self.llcrnry); p2 = (self.urcrnrx,self.urcrnry)
p3 = (self.llcrnrx,self.urcrnry); p4 = (self.urcrnrx,self.llcrnry)
for x,y in self.coastpolygons:
xa = N.array(x,'f')
ya = N.array(y,'f')
# clip to map domain.
xa = N.clip(xa, self.xmin, self.xmax)
ya = N.clip(ya, self.ymin, self.ymax)
# check to see if all four corners of domain in polygon (if so,
# don't draw since it will just fill in the whole map).
test1 = N.fabs(xa-self.xmax) < 10.
test2 = N.fabs(xa-self.xmin) < 10.
test3 = N.fabs(ya-self.ymax) < 10.
test4 = N.fabs(ya-self.ymin) < 10.
hasp1 = sum(test1*test3)
hasp2 = sum(test2*test3)
hasp4 = sum(test2*test4)
hasp3 = sum(test1*test4)
if not hasp1 or not hasp2 or not hasp3 or not hasp4:
xy = zip(xa.tolist(),ya.tolist())
poly = Polygon(xy,facecolor=color,edgecolor=color,linewidth=0)
ax.add_patch(poly)
def fillcontinents(self, ax, color=0.8):
"""
Fill continents.
ax - current axis instance.
color - color to fill continents (default gray).
"""
# define corners of map domain.
p1 = (self.llcrnrx, self.llcrnry)
p2 = (self.urcrnrx, self.urcrnry)
p3 = (self.llcrnrx, self.urcrnry)
p4 = (self.urcrnrx, self.llcrnry)
for x, y in self.coastpolygons:
xa = N.array(x, 'f')
ya = N.array(y, 'f')
# clip to map domain.
xa = N.clip(xa, self.xmin, self.xmax)
ya = N.clip(ya, self.ymin, self.ymax)
# check to see if all four corners of domain in polygon (if so,
# don't draw since it will just fill in the whole map).
test1 = N.fabs(xa - self.xmax) < 10.
test2 = N.fabs(xa - self.xmin) < 10.
test3 = N.fabs(ya - self.ymax) < 10.
test4 = N.fabs(ya - self.ymin) < 10.
hasp1 = sum(test1 * test3)
hasp2 = sum(test2 * test3)
hasp4 = sum(test2 * test4)
hasp3 = sum(test1 * test4)
if not hasp1 or not hasp2 or not hasp3 or not hasp4:
xy = zip(xa.tolist(), ya.tolist())
poly = Polygon(xy,
facecolor=color,
edgecolor=color,
linewidth=0)
ax.add_patch(poly)
def _mad2(data, included):
# adapted from NR function moment
# same as mad3 but ignores zero values
n = len(data[:,0,0])
s = num.zeros(data[0,:,:].shape, num.Float32)
# First pass to get the mean.
for j in range(n): s += data[j,:,:]
ave = s/included
adev = num.zeros(s.shape, num.Float32)
for j in range(n):
d = where(num.fabs(data[j]) > tiny,
num.fabs(data[j] - ave),
0.0)
num.add(adev, d, adev)
return adev / included
def _simple_logistic_regression(x,y,beta_start=None,verbose=False,
CONV_THRESH=1.e-3,MAXIT=500):
"""
Faster than logistic_regression when there is only one predictor.
"""
if len(x) != len(y):
raise ValueError, "x and y should be the same length!"
if beta_start is None:
beta_start = NA.zeros(2,x.typecode())
iter = 0; diff = 1.; beta = beta_start # initial values
if verbose:
print 'iteration beta log-likliehood |beta-beta_old|'
while iter < MAXIT:
beta_old = beta
p = NA.exp(beta[0]+beta[1]*x)/(1.+NA.exp(beta[0]+beta[1]*x))
l = NA.sum(y*NA.log(p) + (1.-y)*NA.log(1.-p)) # log-likliehood
s = NA.array([NA.sum(y-p), NA.sum((y-p)*x)]) # scoring function
# information matrix
J_bar = NA.array([[NA.sum(p*(1-p)),NA.sum(p*(1-p)*x)],
[NA.sum(p*(1-p)*x),NA.sum(p*(1-p)*x*x)]])
beta = beta_old + NA.dot(LA.inverse(J_bar),s) # new value of beta
diff = NA.sum(NA.fabs(beta-beta_old)) # sum of absolute differences
if verbose:
print iter+1, beta, l, diff
if diff <= CONV_THRESH: break
iter = iter + 1
return beta, J_bar, l
def _simple_logistic_regression(x,y,beta_start=None,verbose=False,
CONV_THRESH=1.e-3,MAXIT=500):
"""
Faster than logistic_regression when there is only one predictor.
"""
if len(x) != len(y):
raise ValueError, "x and y should be the same length!"
if beta_start is None:
beta_start = NA.zeros(2,x.dtype.char)
iter = 0; diff = 1.; beta = beta_start # initial values
if verbose:
print 'iteration beta log-likliehood |beta-beta_old|'
while iter < MAXIT:
beta_old = beta
p = NA.exp(beta[0]+beta[1]*x)/(1.+NA.exp(beta[0]+beta[1]*x))
l = NA.sum(y*NA.log(p) + (1.-y)*NA.log(1.-p)) # log-likliehood
s = NA.array([NA.sum(y-p), NA.sum((y-p)*x)]) # scoring function
# information matrix
J_bar = NA.array([[NA.sum(p*(1-p)),NA.sum(p*(1-p)*x)],
[NA.sum(p*(1-p)*x),NA.sum(p*(1-p)*x*x)]])
beta = beta_old + NA.dot(LA.inverse(J_bar),s) # new value of beta
diff = NA.sum(NA.fabs(beta-beta_old)) # sum of absolute differences
if verbose:
print iter+1, beta, l, diff
if diff <= CONV_THRESH: break
iter = iter + 1
return beta, J_bar, l
def _mad3(data):
# adapted from NR function moment
n = len(data[:,0,0])
s = num.zeros(data[0,:,:].shape, num.Float32)
# First pass to get the mean.
for j in range(n): s += data[j,:,:]
ave = s/n
adev = num.zeros(s.shape, num.Float32)
for j in range(n):
num.add(adev, num.fabs(data[j] - ave), adev)
return adev / n
def logistic_regression(x,
y,
beta_start=None,
verbose=False,
CONV_THRESH=1.e-3,
MAXIT=500):
"""
Uses the Newton-Raphson algorithm to calculate a maximum
likelihood estimate logistic regression.
The algorithm is known as 'iteratively re-weighted least squares', or IRLS.
x - rank-1 or rank-2 array of predictors. If x is rank-2,
the number of predictors = x.shape[0] = N. If x is rank-1,
it is assumed N=1.
y - binary outcomes (if N>1 len(y) = x.shape[1], if N=1 len(y) = len(x))
beta_start - initial beta vector (default zeros(N+1,x.dtype.char))
if verbose=True, diagnostics printed for each iteration (default False).
MAXIT - max number of iterations (default 500)
CONV_THRESH - convergence threshold (sum of absolute differences
of beta-beta_old, default 0.001)
returns beta (the logistic regression coefficients, an N+1 element vector),
J_bar (the (N+1)x(N+1) information matrix), and l (the log-likeliehood).
J_bar can be used to estimate the covariance matrix and the standard
error for beta.
l can be used for a chi-squared significance test.
covmat = inverse(J_bar) --> covariance matrix of coefficents (beta)
stderr = sqrt(diag(covmat)) --> standard errors for beta
deviance = -2l --> scaled deviance statistic
chi-squared value for -2l is the model chi-squared test.
"""
if x.shape[-1] != len(y):
raise ValueError, "x.shape[-1] and y should be the same length!"
try:
N, npreds = x.shape[1], x.shape[0]
except: # single predictor, use simple logistic regression routine.
return _simple_logistic_regression(x,
y,
beta_start=beta_start,
CONV_THRESH=CONV_THRESH,
MAXIT=MAXIT,
verbose=verbose)
if beta_start is None:
beta_start = NA.zeros(npreds + 1, x.dtype.char)
X = NA.ones((npreds + 1, N), x.dtype.char)
X[1:, :] = x
Xt = NA.transpose(X)
iter = 0
diff = 1.
beta = beta_start # initial values
if verbose:
print 'iteration beta log-likliehood |beta-beta_old|'
while iter < MAXIT:
beta_old = beta
ebx = NA.exp(NA.dot(beta, X))
p = ebx / (1. + ebx)
l = NA.sum(y * NA.log(p) +
(1. - y) * NA.log(1. - p)) # log-likeliehood
s = NA.dot(X, y - p) # scoring function
J_bar = NA.dot(X * p, Xt) # information matrix
beta = beta_old + NA.dot(LA.inverse(J_bar), s) # new value of beta
diff = NA.sum(NA.fabs(beta - beta_old)) # sum of absolute differences
if verbose:
print iter + 1, beta, l, diff
if diff <= CONV_THRESH: break
iter = iter + 1
if iter == MAXIT and diff > CONV_THRESH:
print 'warning: convergence not achieved with threshold of %s in %s iterations' % (
CONV_THRESH, MAXIT)
return beta, J_bar, l
def drawmeridians(self,ax,meridians,color='k',linewidth=1., \
linestyle='--',dashes=[1,1],labels=[0,0,0,0],\
font='rm',fontsize=12):
"""
draw meridians (longitude lines).
ax - current axis instance.
meridians - list containing longitude values to draw (in degrees).
color - color to draw meridians (default black).
linewidth - line width for meridians (default 1.)
linestyle - line style for meridians (default '--', i.e. dashed).
dashes - dash pattern for meridians (default [1,1], i.e. 1 pixel on,
1 pixel off).
labels - list of 4 values (default [0,0,0,0]) that control whether
meridians are labelled where they intersect the left, right, top or
bottom of the plot. For example labels=[1,0,0,1] will cause meridians
to be labelled where they intersect the left and bottom of the plot,
but not the right and top. Labels are located with a precision of 0.1
degrees and are drawn using mathtext.
font - mathtext font used for labels ('rm','tt','it' or 'cal', default 'rm'.
fontsize - font size in points for labels (default 12).
"""
# don't draw meridians past latmax, always draw parallel at latmax.
latmax = 80. # not used for cyl, merc projections.
# offset for labels.
yoffset = (self.urcrnry-self.llcrnry)/100./self.aspect
xoffset = (self.urcrnrx-self.llcrnrx)/100.
if self.projection not in ['merc','cyl']:
lats = N.arange(-latmax,latmax+1).astype('f')
else:
lats = N.arange(-90,91).astype('f')
xdelta = 0.1*(self.xmax-self.xmin)
ydelta = 0.1*(self.ymax-self.ymin)
for merid in meridians:
lons = merid*N.ones(len(lats),'f')
x,y = self(lons,lats)
# remove points outside domain.
testx = N.logical_and(x>=self.xmin-xdelta,x<=self.xmax+xdelta)
x = N.compress(testx, x)
y = N.compress(testx, y)
testy = N.logical_and(y>=self.ymin-ydelta,y<=self.ymax+ydelta)
x = N.compress(testy, x)
y = N.compress(testy, y)
if len(x) > 1 and len(y) > 1:
# split into separate line segments if necessary.
# (not necessary for mercator or cylindrical).
xd = (x[1:]-x[0:-1])**2
yd = (y[1:]-y[0:-1])**2
dist = N.sqrt(xd+yd)
split = dist > 500000.
if N.sum(split) and self.projection not in ['merc','cyl']:
ind = (N.compress(split,pylab.squeeze(split*N.indices(xd.shape)))+1).tolist()
xl = []
yl = []
iprev = 0
ind.append(len(xd))
for i in ind:
xl.append(x[iprev:i])
yl.append(y[iprev:i])
iprev = i
else:
xl = [x]
yl = [y]
# draw each line segment.
for x,y in zip(xl,yl):
# skip if only a point.
if len(x) > 1 and len(y) > 1:
l = Line2D(x,y,linewidth=linewidth,linestyle=linestyle)
l.set_color(color)
l.set_dashes(dashes)
ax.add_line(l)
# draw labels for meridians.
# search along edges of map to see if parallels intersect.
# if so, find x,y location of intersection and draw a label there.
if self.projection == 'cyl':
dx = 0.01; dy = 0.01
elif self.projection == 'merc':
dx = 0.01; dy = 1000
else:
dx = 1000; dy = 1000
for dolab,side in zip(labels,['l','r','t','b']):
if not dolab: continue
# for cyl or merc, don't draw meridians on left or right.
if self.projection in ['cyl','merc'] and side in ['l','r']: continue
if side in ['l','r']:
nmax = int((self.ymax-self.ymin)/dy+1)
if self.urcrnry < self.llcrnry:
yy = self.llcrnry-dy*N.arange(nmax)
else:
yy = self.llcrnry+dy*N.arange(nmax)
if side == 'l':
lons,lats = self(self.llcrnrx*N.ones(yy.shape,'f'),yy,inverse=True)
else:
lons,lats = self(self.urcrnrx*N.ones(yy.shape,'f'),yy,inverse=True)
lons = N.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
else:
nmax = int((self.xmax-self.xmin)/dx+1)
if self.urcrnrx < self.llcrnrx:
xx = self.llcrnrx-dx*N.arange(nmax)
else:
xx = self.llcrnrx+dx*N.arange(nmax)
if side == 'b':
lons,lats = self(xx,self.llcrnry*N.ones(xx.shape,'f'),inverse=True)
else:
lons,lats = self(xx,self.urcrnry*N.ones(xx.shape,'f'),inverse=True)
lons = N.where(lons < 0, lons+360, lons)
lons = [int(lon*10) for lon in lons.tolist()]
lats = [int(lat*10) for lat in lats.tolist()]
for lon in meridians:
if lon<0: lon=lon+360.
# find index of meridian (there may be two, so
# search from left and right).
try:
nl = lons.index(int(lon*10))
except:
nl = -1
try:
nr = len(lons)-lons[::-1].index(int(lon*10))-1
except:
nr = -1
if lon>180:
lonlab = r'$\%s{%g\/^{\circ}\/W}$'%(font,N.fabs(lon-360))
elif lon<180 and lon != 0:
lonlab = r'$\%s{%g\/^{\circ}\/E}$'%(font,lon)
else:
lonlab = r'$\%s{%g\/^{\circ}}$'%(font,lon)
# meridians can intersect each map edge twice.
for i,n in enumerate([nl,nr]):
lat = lats[n]/10.
# no meridians > latmax for projections other than merc,cyl.
if self.projection not in ['merc','cyl'] and lat > latmax: continue
# don't bother if close to the first label.
if i and abs(nr-nl) < 100: continue
if n > 0:
if side == 'l':
pylab.text(self.llcrnrx-xoffset,yy[n],lonlab,horizontalalignment='right',verticalalignment='center',fontsize=fontsize)
elif side == 'r':
pylab.text(self.urcrnrx+xoffset,yy[n],lonlab,horizontalalignment='left',verticalalignment='center',fontsize=fontsize)
elif side == 'b':
pylab.text(xx[n],self.llcrnry-yoffset,lonlab,horizontalalignment='center',verticalalignment='top',fontsize=fontsize)
else:
pylab.text(xx[n],self.urcrnry+yoffset,lonlab,horizontalalignment='center',verticalalignment='bottom',fontsize=fontsize)
# make sure axis ticks are turned off
ax.set_xticks([])
ax.set_yticks([])
def drawmeridians(self,ax,meridians,color='k',linewidth=1., \
linestyle='--',dashes=[1,1],labels=[0,0,0,0],\
font='rm',fontsize=12):
"""
draw meridians (longitude lines).
ax - current axis instance.
meridians - list containing longitude values to draw (in degrees).
color - color to draw meridians (default black).
linewidth - line width for meridians (default 1.)
linestyle - line style for meridians (default '--', i.e. dashed).
dashes - dash pattern for meridians (default [1,1], i.e. 1 pixel on,
1 pixel off).
labels - list of 4 values (default [0,0,0,0]) that control whether
meridians are labelled where they intersect the left, right, top or
bottom of the plot. For example labels=[1,0,0,1] will cause meridians
to be labelled where they intersect the left and bottom of the plot,
but not the right and top. Labels are located with a precision of 0.1
degrees and are drawn using mathtext.
font - mathtext font used for labels ('rm','tt','it' or 'cal', default 'rm'.
fontsize - font size in points for labels (default 12).
"""
# don't draw meridians past latmax, always draw parallel at latmax.
latmax = 80. # not used for cyl, merc projections.
# offset for labels.
yoffset = (self.urcrnry - self.llcrnry) / 100. / self.aspect
xoffset = (self.urcrnrx - self.llcrnrx) / 100.
if self.projection not in ['merc', 'cyl']:
lats = N.arange(-latmax, latmax + 1).astype('f')
else:
lats = N.arange(-90, 91).astype('f')
xdelta = 0.1 * (self.xmax - self.xmin)
ydelta = 0.1 * (self.ymax - self.ymin)
for merid in meridians:
lons = merid * N.ones(len(lats), 'f')
x, y = self(lons, lats)
# remove points outside domain.
testx = N.logical_and(x >= self.xmin - xdelta,
x <= self.xmax + xdelta)
x = N.compress(testx, x)
y = N.compress(testx, y)
testy = N.logical_and(y >= self.ymin - ydelta,
y <= self.ymax + ydelta)
x = N.compress(testy, x)
y = N.compress(testy, y)
if len(x) > 1 and len(y) > 1:
# split into separate line segments if necessary.
# (not necessary for mercator or cylindrical).
xd = (x[1:] - x[0:-1])**2
yd = (y[1:] - y[0:-1])**2
dist = N.sqrt(xd + yd)
split = dist > 500000.
if N.sum(split) and self.projection not in ['merc', 'cyl']:
ind = (N.compress(
split, pylab.squeeze(split * N.indices(xd.shape))) +
1).tolist()
xl = []
yl = []
iprev = 0
ind.append(len(xd))
for i in ind:
xl.append(x[iprev:i])
yl.append(y[iprev:i])
iprev = i
else:
xl = [x]
yl = [y]
# draw each line segment.
for x, y in zip(xl, yl):
# skip if only a point.
if len(x) > 1 and len(y) > 1:
l = Line2D(x,
y,
linewidth=linewidth,
linestyle=linestyle)
l.set_color(color)
l.set_dashes(dashes)
ax.add_line(l)
# draw labels for meridians.
# search along edges of map to see if parallels intersect.
# if so, find x,y location of intersection and draw a label there.
if self.projection == 'cyl':
dx = 0.01
dy = 0.01
elif self.projection == 'merc':
dx = 0.01
dy = 1000
else:
dx = 1000
dy = 1000
for dolab, side in zip(labels, ['l', 'r', 't', 'b']):
if not dolab: continue
# for cyl or merc, don't draw meridians on left or right.
if self.projection in ['cyl', 'merc'] and side in ['l', 'r']:
continue
if side in ['l', 'r']:
nmax = int((self.ymax - self.ymin) / dy + 1)
if self.urcrnry < self.llcrnry:
yy = self.llcrnry - dy * N.arange(nmax)
else:
yy = self.llcrnry + dy * N.arange(nmax)
if side == 'l':
lons, lats = self(self.llcrnrx * N.ones(yy.shape, 'f'),
yy,
inverse=True)
else:
lons, lats = self(self.urcrnrx * N.ones(yy.shape, 'f'),
yy,
inverse=True)
lons = N.where(lons < 0, lons + 360, lons)
lons = [int(lon * 10) for lon in lons.tolist()]
lats = [int(lat * 10) for lat in lats.tolist()]
else:
nmax = int((self.xmax - self.xmin) / dx + 1)
if self.urcrnrx < self.llcrnrx:
xx = self.llcrnrx - dx * N.arange(nmax)
else:
xx = self.llcrnrx + dx * N.arange(nmax)
if side == 'b':
lons, lats = self(xx,
self.llcrnry * N.ones(xx.shape, 'f'),
inverse=True)
else:
lons, lats = self(xx,
self.urcrnry * N.ones(xx.shape, 'f'),
inverse=True)
lons = N.where(lons < 0, lons + 360, lons)
lons = [int(lon * 10) for lon in lons.tolist()]
lats = [int(lat * 10) for lat in lats.tolist()]
for lon in meridians:
if lon < 0: lon = lon + 360.
# find index of meridian (there may be two, so
# search from left and right).
try:
nl = lons.index(int(lon * 10))
except:
nl = -1
try:
nr = len(lons) - lons[::-1].index(int(lon * 10)) - 1
except:
nr = -1
if lon > 180:
lonlab = r'$\%s{%g\/^{\circ}\/W}$' % (font,
N.fabs(lon - 360))
elif lon < 180 and lon != 0:
lonlab = r'$\%s{%g\/^{\circ}\/E}$' % (font, lon)
else:
lonlab = r'$\%s{%g\/^{\circ}}$' % (font, lon)
# meridians can intersect each map edge twice.
for i, n in enumerate([nl, nr]):
lat = lats[n] / 10.
# no meridians > latmax for projections other than merc,cyl.
if self.projection not in ['merc', 'cyl'] and lat > latmax:
continue
# don't bother if close to the first label.
if i and abs(nr - nl) < 100: continue
if n > 0:
if side == 'l':
pylab.text(self.llcrnrx - xoffset,
yy[n],
lonlab,
horizontalalignment='right',
verticalalignment='center',
fontsize=fontsize)
elif side == 'r':
pylab.text(self.urcrnrx + xoffset,
yy[n],
lonlab,
horizontalalignment='left',
verticalalignment='center',
fontsize=fontsize)
elif side == 'b':
pylab.text(xx[n],
self.llcrnry - yoffset,
lonlab,
horizontalalignment='center',
verticalalignment='top',
fontsize=fontsize)
else:
pylab.text(xx[n],
self.urcrnry + yoffset,
lonlab,
horizontalalignment='center',
verticalalignment='bottom',
fontsize=fontsize)
# make sure axis ticks are turned off
ax.set_xticks([])
ax.set_yticks([])
def logistic_regression(x,y,beta_start=None,verbose=False,CONV_THRESH=1.e-3,
MAXIT=500):
"""
Uses the Newton-Raphson algorithm to calculate a maximum
likelihood estimate logistic regression.
The algorithm is known as 'iteratively re-weighted least squares', or IRLS.
x - rank-1 or rank-2 array of predictors. If x is rank-2,
the number of predictors = x.shape[0] = N. If x is rank-1,
it is assumed N=1.
y - binary outcomes (if N>1 len(y) = x.shape[1], if N=1 len(y) = len(x))
beta_start - initial beta vector (default zeros(N+1,x.dtype.char))
if verbose=True, diagnostics printed for each iteration (default False).
MAXIT - max number of iterations (default 500)
CONV_THRESH - convergence threshold (sum of absolute differences
of beta-beta_old, default 0.001)
returns beta (the logistic regression coefficients, an N+1 element vector),
J_bar (the (N+1)x(N+1) information matrix), and l (the log-likeliehood).
J_bar can be used to estimate the covariance matrix and the standard
error for beta.
l can be used for a chi-squared significance test.
covmat = inverse(J_bar) --> covariance matrix of coefficents (beta)
stderr = sqrt(diag(covmat)) --> standard errors for beta
deviance = -2l --> scaled deviance statistic
chi-squared value for -2l is the model chi-squared test.
"""
if x.shape[-1] != len(y):
raise ValueError, "x.shape[-1] and y should be the same length!"
try:
N, npreds = x.shape[1], x.shape[0]
except: # single predictor, use simple logistic regression routine.
return _simple_logistic_regression(x,y,beta_start=beta_start,
CONV_THRESH=CONV_THRESH,MAXIT=MAXIT,verbose=verbose)
if beta_start is None:
beta_start = NA.zeros(npreds+1,x.dtype.char)
X = NA.ones((npreds+1,N), x.dtype.char)
X[1:, :] = x
Xt = NA.transpose(X)
iter = 0; diff = 1.; beta = beta_start # initial values
if verbose:
print 'iteration beta log-likliehood |beta-beta_old|'
while iter < MAXIT:
beta_old = beta
ebx = NA.exp(NA.dot(beta, X))
p = ebx/(1.+ebx)
l = NA.sum(y*NA.log(p) + (1.-y)*NA.log(1.-p)) # log-likeliehood
s = NA.dot(X, y-p) # scoring function
J_bar = NA.dot(X*p,Xt) # information matrix
beta = beta_old + NA.dot(LA.inverse(J_bar),s) # new value of beta
diff = NA.sum(NA.fabs(beta-beta_old)) # sum of absolute differences
if verbose:
print iter+1, beta, l, diff
if diff <= CONV_THRESH: break
iter = iter + 1
if iter == MAXIT and diff > CONV_THRESH:
print 'warning: convergence not achieved with threshold of %s in %s iterations' % (CONV_THRESH,MAXIT)
return beta, J_bar, l
