def fit(self, x_data, y_data, kernel='std', alpha=0.1):
#x_1 = (inv_by_adjugate(midPointMatrix(x.T.dot(x))))
#self._preProcessingData(x,y)
#First part of method minimum square
if (kernel == 'std'):
x = Rdmia.array(x_data)
y = Rdmia.array(y_data)
x_1 = x.T.dot(x).midPointMatrix
x_1 = x_1.I
#y_2 = midPointMatrix(x.T.dot(y))
y_2 = x.T.dot(y).midPointMatrix
#Calculated predictors beta
z = (x_1).dot(y_2)
self._predictors = Rdmia.array(
[z.data[i][0] for i in range(len(z.data))])
elif (kernel == 'multidimensional'):
self._isMult = True
opt_minium = 0.0
opt_maximum = 0.0
for i in np.arange(0.0, 1.0, alpha):
x = Rdmia.arrayAlpha(x_data, i)
y_min, y_max = Rdmia.arrayMinMax(y_data)
x_1 = x.T.dot(x)
x_1 = x_1.I
#y_2 = midPointMatrix(x.T.dot(y))
y_2_min = x.T.dot(y_min)
y_2_max = x.T.dot(y_max)
#Calculated predictors beta
z_min = (x_1).dot(y_2_min)
z_max = (x_1).dot(y_2_max)
#print(z.data)
#self._predictorsLow = (Rdmia.array([z_min.data[i][0] for i in range(len(z_min.data))]))
#self._predictorsUp = (Rdmia.array([z_max.data[i][0] for i in range(len(z_max.data))]))
opt_min, opt_max = self._optimizations(x_data, (Rdmia.array(
[z_min.data[i][0]
for i in range(len(z_min.data))])), (Rdmia.array(
[z_max.data[i][0] for i in range(len(z_max.data))])),
y_min, y_max)
if (i == 0):
self._predictorsLow = (Rdmia.array(
[z_min.data[i][0] for i in range(len(z_min.data))]))
self._predictorsUp = (Rdmia.array(
[z_max.data[i][0] for i in range(len(z_max.data))]))
opt_minimum = opt_min
opt_maximum = opt_max
elif (opt_min < opt_minium):
self._predictorsLow = (Rdmia.array(
[z_min.data[i][0] for i in range(len(z_min.data))]))
opt_minimum = opt_min
elif (opt_max < opt_maximum):
self._predictorsUp = (Rdmia.array(
[z_max.data[i][0] for i in range(len(z_max.data))]))
opt_maximum = opt_max
#Calculte Status
y_pred = self.predict(x_data)
self._coefDetermination = self._coefDetermination(y_data, y_pred)
self._rmse = self._RMSE(y_data, y_pred)
print("### STATISTICS OF MODEL ###")
print("# RMSE: ", self._rmse)
print("# Coefficiente of Determination: ", self._coefDetermination)
print("#")
[Rdmia.number(70,112)],
[Rdmia.number(54,72)],
[Rdmia.number(70,100)],
[Rdmia.number(63,75)],
[Rdmia.number(72,100)],
[Rdmia.number(76,98)],
[Rdmia.number(86,96)],
[Rdmia.number(86,100)]
])
y = [[Rdmia.number(44,68)],
[Rdmia.number(60,72)],
[Rdmia.number(56,90)],
[Rdmia.number(70,112)],
[Rdmia.number(54,72)],
[Rdmia.number(70,100)],
[Rdmia.number(63,75)],
[Rdmia.number(72,100)],
[Rdmia.number(76,98)],
[Rdmia.number(86,96)],
[Rdmia.number(86,100)]
]
'''
y = Rdmia.array([[25], [13], [8], [20]])
regressor = RLS()
regressor.fit(x, y)
ypred = regressor.predict(y)
print(regressor)