I'm setting up a linearregression model on my data set but I am encountering an attribute error which I'm having an issue resolving.
class LinearRegressionGD (object):
def _init_(self, eta=0.001, n_iter=20):
self.eta = eta
self.n_iter = n_iter
def fit(self, X, y):
self.w = np.zeros(1 + X.shape[1])
self.cost_ = {}
for i in range(self.n_iter):
output = self.net_input (X)
errors = (y - output)
self.w_[1:] += self.eta * X.T.dot(errors)
self.w_[0] += self.eta * errors.sum()
cost = (errors**2).sum() / 2.0
self.cost_.append(cost)
return self
def net_input(self, X):
return np.dot(X, self.w_[1:]) + self.w_[0]
def predict(self, X):
return self.net_input(X)
X = racing[["BSP"]].values
y = racing[["Position"]].values
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X_std = sc_X.fit_transform(X)
y_std = sc_y.fit_transform(y)
lr = LinearRegressionGD()
lr.fit(X_std, y_std)
I then expected t be able plot the results to see if the linear regresion had converged but I am getting the following error:
AttributeError Traceback (most recent call last)
<ipython-input-23-c876c2ee7b9e> in <module>
----> 1 class LinearRegressionGD (object):
2
3 def _init_(self, eta=0.001, n_iter=20):
4 self.eta = eta
5 self.n_iter = n_iter
<ipython-input-23-c876c2ee7b9e> in LinearRegressionGD()
32 y_std = sc_y.fit_transform(y)
33 lr = LinearRegressionGD()
---> 34 lr.fit(X_std, y_std)
<ipython-input-22-19842f46cb51> in fit(self, X, y)
9 self.cost_ = {}
10
---> 11 for i in range(self.n_iter):
12 output = self.net_input (X)
13 errors = (y - output)
AttributeError: 'LinearRegressionGD' object has no attribute 'n_iter'
You have to write constructor name using 2 underscores before and 2 underscores after init
: __init__()
That _init_()
function you wrote doesn't run when you create the object, so the object doesn't get any variable named n_iter
to use with.