def _preProcessingData(self, x, y):
for i in range(len(y.data)):
for k in range(len(x.data[0])):
if (type(x.data[i][k]) is not Rdm):
x.data[i][k] = Rdmia.number(x.data[i][k])
if (type(y.data[i]) is not Rdm):
y.data[i][0] = Rdmia.number(y.data[i][0])
def rosenbrock(x): # rosen.m
""" http://en.wikipedia.org/wiki/Rosenbrock_function """
# a sum of squares, so LevMar (scipy.optimize.leastsq) is pretty good
x = np.asarray_chkfinite(x)
x0 = x[:-1]
x1 = x[1:]
return (sum((1.0 - x0)**2.0) + rdmia.number(100.0) * sum(
(x1 - x0**2.0)**2.0))
def makeList(steps, dim):
result = []
r = []
for k in steps:
for i in range(dim):
r.append(rdmia.number(k))
result.append(r)
r = []
return result
def predict(self, x):
r_list = []
if (self._isMult):
for val in range(len(x)):
resultLow = sum([
self._predictorsLow.data[i] * x[val][i].lower()
for i in range(len(self._predictorsLow.data))
])
resultUp = sum([
self._predictorsUp.data[i] * x[val][i].upper()
for i in range(len(self._predictorsUp.data))
])
r_list.append(Rdmia.number(resultLow, resultUp))
else:
for val in range(len(x)):
result = sum([
self._predictors.data[i] * x[val][i]
for i in range(len(self._predictors.data))
])
r_list.append(result)
#r = qm.midpoint(self._predictors.data[0] + self._predictors.data[1]*x.data[val][1])
return r_list
def _mean(self, d):
mean = Rdmia.number(0.0)
for val in d:
mean += val[0]
return mean / len(d)