def run(tday, history, logfile):
logstr = ''
#
w, x, y = history.grab(nsample, ntracks, tday + 1)
x = xfilter(x)
timer = timer_c()
lsvm = svmutil.svm_train(y, x, options)
#svmutil.svm_save_model('benchmark.svm', lsvm)
#lsvm = svmutil.svm_load_model('benchmark.svm')
print 'svm trained successfully in %s sec.' % (str(
float('{0:.3f}'.format(timer.lag()))))
#
timer = timer_c()
p_labels, p_acc, p_vals = svmutil.svm_predict(y, x, lsvm, '')
print 'svm predicted successfully in %s sec.' % (str(
float('{0:.3f}'.format(timer.lag()))))
plotdraw(p_labels, y)
#
timer = timer_c()
w, x, y = history.grab(1, ntracks, tday)
x = xfilter(x)
p_labels, p_acc, p_vals = svmutil.svm_predict(y, x, lsvm, '')
print 'svm predicted successfully in %s sec.' % (str(
float('{0:.3f}'.format(timer.lag()))))
plotdraw(p_labels, y)
def run(history):
#
w, x, y = history.grab(nsample, ntracks, 0)
timer = timer_c()
lsvm = svmutil.svm_train(y, x, options)
svmutil.svm_save_model('temp.svm', lsvm)
print 'svm trained successfully in %s sec.' % (str(float('{0:.3f}'.format(timer.lag()))))
#
timer = timer_c()
p_labels, p_acc, p_vals = svmutil.svm_predict(y, x, lsvm, '')
print 'svm predicted successfully in %s sec.' % (str(float('{0:.3f}'.format(timer.lag()))))
plotdraw(p_labels, y)
def run(history, tweek, pref):
#
timer = timer_c()
lsvm = svmutil.svm_load_model('temp.svm')
print 'svm loaded successfully in %s sec.' % (str(float('{0:.3f}'.format(timer.lag()))))
#
w, x, y = history.grab(1, ntracks, tweek-1, pref)
x = xfilter(x)
timer.reset()
p_labels, p_acc, p_vals = svmutil.svm_predict(y, x, lsvm, '')
print 'svm predicted successfully in %s sec with %d samples.' % (str(float('{0:.3f}'.format(timer.lag()))), len(w))
plotdraw(p_labels, y)
#
foo = []
for i in xrange(len(w)):
foo.append((w[i], p_labels[i], y[i]))
foo.sort(key = lambda tup: (tup[1]))
for row in foo:
print '%s y\' = %.6f, y = %.6f' % row