Xtest,
ytest,
title="Least Squares, with bias",
filename="least_squares_bias.pdf")
elif question == "3.2":
data = load_dataset("basisData.pkl")
X = data['X']
y = data['y']
Xtest = data['Xtest']
ytest = data['ytest']
for p in range(11):
print("p = %d" % p)
''' YOUR CODE HERE '''
model = linear_model.LeastSquaresPoly(p)
model.fit(X, y)
test_and_plot(model,
X,
y,
Xtest,
ytest,
title='Least Squares Polynomial p = %d' % p,
filename="PolyBasis%d.pdf" % p)
elif question == "4":
data = load_dataset("basisData.pkl")
X = data['X']
y = data['y']
Xtest = data['Xtest']
def calculate_poly_w(X, y):
model = linear_model.LeastSquaresPoly(p=5)
model.fit(X, y)
w = model.print_w()
return w
# print(np.shape(X_world_cases))
# print(np.shape(y_world))
model = linear_model.LeastSquares()
w = model.fit(X_world_cases, y_world)
y_pred = model.predict(X_world_cases)
utils.test_and_plot(model,
X_world_cases,
y_world,
Xtest=None,
ytest=None,
title="World",
filename="World_cases_feature.pdf")
print("Poly_Canadian: ")
model = linear_model.LeastSquaresPoly(p=5)
w = model.fit(X_can_cases, y_can)
y_pred = model.predict(X_can_cases)
utils.test_and_plot(model,
X_can_cases,
y_can,
Xtest=None,
ytest=None,
title="Canadian Poly",
filename="Canadian_cases_feature_poly.pdf")
print(f["country_id"].unique())
model1 = linear_model.LeastSquaresPoly(p=5)
else:
maxcorr, _ = pearsonr(train_ref_dataset, train_exp_dataset)
print("Correlation =", maxcorr)
train_ref_dataset = utils.shift_fill0(train_ref_dataset, lag)
X = utils.s2v(train_ref_dataset)
y = utils.s2v(train_exp_dataset)
Xtest = utils.s2v(ref_dataset[train_day:])
ytest = utils.s2v(exp_dataset[train_day:])
poly_par = 1
if test_par != None:
poly_par = int(test_par)
model = linear_model.LeastSquaresPoly(p=poly_par)
model.fit(X, y)
titlename = exp_name + "_" + exp_ct_name + "_vs_" + ref_name + "_" + ref_ct_name + "_lag" + str(
lag) + "_p" + str(poly_par)
filename = "LRplot_" + titlename + ".pdf"
utils.test_and_plot(model,
X,
y,
Xtest,
ytest,
title=titlename,
filename=filename)
if question == "corr":
dataset = read_dataset("phase1_training_data.csv")
