def interation(self, inputs, target, lRate, oscillations, lwp):
if (printT):
print("in: ", inputs, " target: ", target)
for i in self.nodes:
node = self.nodes[i]
weightsOld = node.weights
node.weights = {
parentNodeId: node.weights[parentNodeId] + (lRate * node.delta_Wi(inputs, self.nodes, target, parentNodeId))
for parentNodeId in node.weights}
for id in node.weights:
if (nan(node.weights[id])):
print("NaN WEight ", id)
node.weights[id] = 0
for i in self.nodes:
if (i == 'out'):
out = self.nodes[i]
if (out.hasNan):
e = float('nan')
else:
try:
e = (target - out.out) ** 2
except OverflowError:
e = float('nan')
if (lwp and out.delta_E_out < 0):
lwp = False
oscillations += 1
elif (not lwp and out.delta_E_out > 0):
lwp = True
oscillations += 1
self.nodes[i].cleanUp()
if (oscillations > 5):
lRate *= 0.9
oscillations = 0
return (lRate, oscillations, lwp, e)
def add_data():
columns = docentes
resumenes = []
# Resumenes Docentes Agregando Data a la BD
for index, resumen in docentes.iterrows():
data = {}
for column in columns:
try:
value = int(
resumen[column]) if not nan(resumen[column]) else None
except:
value = resumen[column]
data[column] = value
data['tipo_resumen'] = 'docentes'
investigacion = Investigacion(data) # Creamos el Objeto
resumenes.append(investigacion)
# db.session.add(investigacion) # Agregamos a la session
# del docentes
# del resumenes_docentes
# Resumenes Estudiantes Agregando Data a la BD
for index, resumen in estudiantes.iterrows():
data = {}
for column in columns:
try:
value = int(
resumen[column]) if not nan(resumen[column]) else None
except:
value = resumen[column]
data[column] = value
data['tipo_resumen'] = 'estudiantes'
investigacion = Investigacion(data) # Creamos el Objeto
resumenes.append(investigacion)
# db.session.add(investigacion)
# del estudiantes
# del resumenes_estudiantes
db.session.add_all(resumenes)
db.session.commit() # Realizamos la operacion atomica
# del resumenes
print('Base de datos con Resumenes')
def __attributes_setter__(self, values):
if self.id is not None:
del values['id']
for attribute in values.keys():
try:
value = int(values[attribute]) if not nan(values[attribute]) else None
except:
value = values[attribute]
setattr(self, attribute, value)
def getOut(self, inputs, nodes):
if (self.out != False):
return self.out
if (len(self.parents) == 0):
self.out = inputs[self.id]
else:
valueWeightTuples = [(nodes[i].getOut(inputs, nodes), self.weights[i]) for i in self.parents]
self.out = self.function.calc(valueWeightTuples)
self.hasNan = False
if (nan(self.out)):
print("NAN in ", self.id)
self.hasNan = True
self.out = 0
return self.out
def sumLocation(self, point):
"""Update the total moment about a new location."""
# make sure that the supplied point is a list of three numbers
if type(point) != list and len(point) != 3:
raise TypeError("Point must be a three-item list of numbers")
else:
for coord in point:
if math.nan(coord):
raise TypeError("""Point must be a three-item list of
numbers""")
self.__sumLocation = point
self.__update()
print("lets go")
fetcher = PersistentFetcher(
"/home/ps/PycharmProjects/evo-feature-engineer/data")
dataProvider = DataProvider(fetcher)
data = dataProvider.getData(None, None)
net = generate(data, 9)
lRate, oscillations, lwp, mse = 0.001, 0, True, 101
iters = 0
while (mse > 1 and iters < 10):
print("ITERATION: ", iters)
sum_e = 0
nans = 0
for d in data:
lRate, oscillations, lwp, e = net.interation(d.x, d.y, lRate,
oscillations, lwp)
if (nan(e)):
nans += 1
else:
sum_e += e
iters += 1
valids = len(data) - nans
mse = sum_e / (valids) if valids > 0 else -1
print("NaNs: ", nans)
print("mean absolute error: ", math.sqrt(mse))
mean = sum([d.y for d in data]) / len(data)
print("Mean ", mean)
var = sum([(d.y - mean)**2 for d in data]) / len(data)
print("Standard variantion, ", math.sqrt(var))
print("Variance ,", var)
pred = net.predict(data[0].x)