item = item_inverse_mapper[ind]
print("item:%s, # of reviews: %1d" % (item, len(X[:, ind].data)))
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.pdf")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
mylist = []
for i in range(0, 400):
mylist.append(1)
for i in range(0, 100):
mylist.append(0.1)
z = np.array(mylist)
print("The item {} has {} reviews".format(
item_code, ratings['item'].value_counts().loc[item_code]))
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.pdf")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
len = len(X)
v = np.full(shape=len, fill_value=1)
v[400:500] = 0.1
V = np.diag(v)
model = linear_model.WeightedLeastSquares()
model.fit(X, y, V)
# YOUR CODE HERE FOR Q1.3
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.pdf")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# YOUR CODE HERE
elif question == "3.3":
# loads the data in the form of dictionary
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
print(
"Number of reviews for the 5 recommendations based on cosine similarity:",
item_record[nearest_index_3[0, 1:]])
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.pdf")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# YOUR CODE HERE
z = np.zeros((500))
z[0:100] = 1
z[100:500] = 0.1
model = linear_model.WeightedLeastSquares()
model.fit(X, y, z)
print(model.w)
X_can_all = np.delete(X_all, [0, 1, 3], axis=1)
X_can_all = np.reshape(X_can_all, (X_can_all.shape[0], 3))
X_can_all = X_can_all.astype(float)
X_can_cases = get_feature(X_all, 2)
X_can_cases_14_100k = get_feature(X_all, 4)
X_can_cases_100k = get_feature(X_all, 5)
model = linear_model.LeastSquares()
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",
filename="Canadian_cases_feature.pdf")
# model.fit(X_cases, y)
# y_pred = model.predict(X_cases)
# model.fit(X_cases, y)
# y_pred = model.predict(X_cases)
# y_pred = model.predict(X_test)
# # y_pred = model.predict(X_cases)
print("World: ")
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")
dataset = utils.get_all(dataset)
n_ct, ct_key, ct_mapper, ct_inv_mapper, ct_ind = utils.make_mapper(
dataset)
day_n = len(dataset[dataset[ct_key] == 'CA']["cases"])
exp_name = "cases_14_100k"
exp_ct_name = 'CA'
reviews = X_binary.getcol(ind).sum()
print(item_inverse_mapper[ind] + " " + str(reviews))
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.pdf")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# YOUR CODE HERE
elif question == "3.3":
# loads the data in the form of dictionary
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
print(X_sums[:][98068])
print(X_sums[:][98066])
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.png")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
dataPoints = np.ones(400)
outliers = np.full((100, ), 0.1)
data = np.concatenate((dataPoints, outliers), axis=0)
z = np.diag(data)
model = linear_model.WeightedLeastSquares()
model.fit(X, y, z)
print(X_sums[:][98068])
print(X_sums[:][98066])
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.pdf")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
R, C = X.shape
# YOUR CODE HERE
z = np.ones(500)
z[400:500] = z[400:500] * .1
d = np.diag(z)
model = linear_model.WeightedLeastSquares()
# YOUR CODE HERE FOR Q1.3
elif question == "3":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# Fit least-squares estimator
model = linear_model.LeastSquares()
model.fit(X, y)
print(model.w)
utils.test_and_plot(model,
X,
y,
title="Least Squares",
filename="least_squares_outliers.pdf")
elif question == "3.1":
data = load_dataset("outliersData.pkl")
X = data['X']
y = data['y']
# YOUR CODE HERE
model = linear_model.WeightedLeastSquares()
model.fit(X[:400], y[:400], 1)
model.fit(X[400:], y[400:], 0.1)
print(model.w)
utils.test_and_plot(model,
