def cvglmnetPlot(cvobject, sign_lambda = 1.0, **options):
sloglam = sign_lambda*scipy.log(cvobject['lambdau'])
fig = plt.gcf()
ax1 = plt.gca()
#fig, ax1 = plt.subplots()
plt.errorbar(sloglam, cvobject['cvm'], cvobject['cvsd'], \
ecolor = (0.5, 0.5, 0.5), \
**options
)
plt.hold(True)
plt.plot(sloglam, cvobject['cvm'], linestyle = 'dashed',\
marker = 'o', markerfacecolor = 'r')
xlim1 = ax1.get_xlim()
ylim1 = ax1.get_ylim()
xval = sign_lambda*scipy.log(scipy.array([cvobject['lambda_min'], cvobject['lambda_min']]))
plt.plot(xval, ylim1, color = 'b', linestyle = 'dashed', \
linewidth = 1)
if cvobject['lambda_min'] != cvobject['lambda_1se']:
xval = sign_lambda*scipy.log([cvobject['lambda_1se'], cvobject['lambda_1se']])
plt.plot(xval, ylim1, color = 'b', linestyle = 'dashed', \
linewidth = 1)
ax2 = ax1.twiny()
ax2.xaxis.tick_top()
atdf = ax1.get_xticks()
indat = scipy.ones(atdf.shape, dtype = scipy.integer)
if sloglam[-1] >= sloglam[1]:
for j in range(len(sloglam)-1, -1, -1):
indat[atdf <= sloglam[j]] = j
else:
for j in range(len(sloglam)):
indat[atdf <= sloglam[j]] = j
prettydf = cvobject['nzero'][indat]
ax2.set(XLim=xlim1, XTicks = atdf, XTickLabels = prettydf)
ax2.grid()
ax1.yaxis.grid()
ax2.set_xlabel('Degrees of Freedom')
# plt.plot(xlim1, [ylim1[1], ylim1[1]], 'b')
# plt.plot([xlim1[1], xlim1[1]], ylim1, 'b')
if sign_lambda < 0:
ax1.set_xlabel('-log(Lambda)')
else:
ax1.set_xlabel('log(Lambda)')
ax1.set_ylabel(cvobject['name'])
#plt.show()
python类ones()的实例源码
def cvglmnetPlot(cvobject, sign_lambda = 1.0, **options):
sloglam = sign_lambda*scipy.log(cvobject['lambdau'])
fig = plt.gcf()
ax1 = plt.gca()
#fig, ax1 = plt.subplots()
plt.errorbar(sloglam, cvobject['cvm'], cvobject['cvsd'], \
ecolor = (0.5, 0.5, 0.5), \
**options
)
plt.hold(True)
plt.plot(sloglam, cvobject['cvm'], linestyle = 'dashed',\
marker = 'o', markerfacecolor = 'r')
xlim1 = ax1.get_xlim()
ylim1 = ax1.get_ylim()
xval = sign_lambda*scipy.log(scipy.array([cvobject['lambda_min'], cvobject['lambda_min']]))
plt.plot(xval, ylim1, color = 'b', linestyle = 'dashed', \
linewidth = 1)
if cvobject['lambda_min'] != cvobject['lambda_1se']:
xval = sign_lambda*scipy.log([cvobject['lambda_1se'], cvobject['lambda_1se']])
plt.plot(xval, ylim1, color = 'b', linestyle = 'dashed', \
linewidth = 1)
ax2 = ax1.twiny()
ax2.xaxis.tick_top()
atdf = ax1.get_xticks()
indat = scipy.ones(atdf.shape, dtype = scipy.integer)
if sloglam[-1] >= sloglam[1]:
for j in range(len(sloglam)-1, -1, -1):
indat[atdf <= sloglam[j]] = j
else:
for j in range(len(sloglam)):
indat[atdf <= sloglam[j]] = j
prettydf = cvobject['nzero'][indat]
ax2.set(XLim=xlim1, XTicks = atdf, XTickLabels = prettydf)
ax2.grid()
ax1.yaxis.grid()
ax2.set_xlabel('Degrees of Freedom')
# plt.plot(xlim1, [ylim1[1], ylim1[1]], 'b')
# plt.plot([xlim1[1], xlim1[1]], ylim1, 'b')
if sign_lambda < 0:
ax1.set_xlabel('-log(Lambda)')
else:
ax1.set_xlabel('log(Lambda)')
ax1.set_ylabel(cvobject['name'])
#plt.show()
def get_fddb_face_data(k = 12, on_drive = False):
root = 'F:\\datasets\\image_data_sets\\faces\\FDDB\\'
iroot = os.path.join(root,'originalPics')
eroot = os.path.join(root,'FDDB-folds')
pattern = '-ellipseList.txt'
c = 0
X,y = [],[]
for path, subdirs, files in os.walk(eroot):
for fname in files:
if fname.find(pattern) > 0:
fpath = os.path.join(path,fname)
print(fpath)
with open(fpath) as f:
lines = sp.array(f.readlines())
paths_indx = sp.where([line.find('/') > 0 for line in lines])[0]
counts_indx = paths_indx + 1
paths = sp.array([e.strip() for e in lines[paths_indx]])
ellipces = []
for i in counts_indx:
cnt = int(lines[i])
ellipces.append(lines[i+1:i+cnt+1])
ellipces = [ [ [float(num) for num in line.split()[:-1]] for line in e] for e in ellipces]
ellipces = sp.array(ellipces)
for iname,ells in zip(paths[:],ellipces[:]):
ppath = os.path.join(iroot,iname.replace('/','\\')) + '.jpg'
file_id = iname.split('/')[-1]
frame = fr.get_frame(ppath)
for item in ells:
ra,rb,theta,x,y = item
x1,y1,x2,y2 = util.ellipse2bbox(a = ra, b = rb, angle = theta, cx = x, cy = y)
x = x1
y = y1
h = abs(y2-y1)
w = abs(x2-x1)
print(file_id,(y,x,h,w))
non_neg = x > 0 and y > 0
if not non_neg:
continue
if on_drive:
for item in Datasets.data_augmentation(frame,y,x,w,h):
fr.write_frame('F:\\train_data\\pos\\' + str(c) + '_' + str(file_id) + '_pos',item)
c +=1
else:
pass
X = sp.array(X)
y = sp.ones(len(X))
return X,y
def fit(self, X, T, max_iter=int(1e2), tol=1e-3, bound=1e10):
"""Fit a RVM model with the training data ``(X, T)``."""
# Initialize the hyperparameters
self._init_hyperparameters(X, T)
# Compute design matrix
n_samples = X.shape[0]
phi = sp.c_[sp.ones(n_samples), self._compute_design_matrix(X)] # Add x0
alpha = self.cov
beta = self.beta
log_evidence = -1e10
for iter in range(max_iter):
alpha[alpha >= bound] = bound
rv_indices = sp.nonzero(alpha < bound)[0]
rv_phi = phi[:, rv_indices]
rv_alpha = alpha[rv_indices]
# Compute the posterior distribution
post_cov = spla.inv(sp.diag(rv_alpha) + beta * sp.dot(rv_phi.T, rv_phi))
post_mean = beta * sp.dot(post_cov, sp.dot(rv_phi.T, T))
# Re-estimate the hyperparameters
gamma = 1 - rv_alpha * post_cov.diagonal()
rv_alpha = gamma / (post_mean * post_mean)
beta = (n_samples + 1 - gamma.sum()) / spla.norm(T - sp.dot(rv_phi, post_mean))**2
# Evalueate the log evidence and test the relative change
C = sp.eye(rv_phi.shape[0]) / beta + rv_phi.dot(sp.diag(1.0 / rv_alpha)).dot(rv_phi.T)
log_evidence_new = -0.5 * (sp.log(spla.det(C)) + T.dot(spla.inv(C)).dot((T)))
diff = spla.norm(log_evidence_new - log_evidence)
if (diff < tol * spla.norm(log_evidence)):
break
log_evidence = log_evidence_new
alpha[rv_indices] = rv_alpha
# Should re-compute the posterior distribution
self.rv_indices = rv_indices
self.cov = post_cov
self.mean = post_mean
self.beta = beta
return self
