import sys
sys.path.append('src')
from polynomial_regressor import PolynomialRegressor
from matrix import Matrix
sandwich_data = Matrix(
elements=[[1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 4, 0], [1, 6, 0], [1, 0, 2],
[1, 0, 4], [1, 0, 6], [1, 0, 8], [1, 2, 2], [1, 3, 4]])
sandwich_ratings = Matrix(
elements=[[1], [2], [4], [8], [9], [2], [5], [7], [6], [0], [0]])
sandwich_regressor = PolynomialRegressor(2)
sandwich_regressor.solve_coefficients_with_inputs(sandwich_data,
sandwich_ratings)
coeffs = sandwich_regressor.coefficients
print('Sandwich Rating Equation')
print('Rating = ', coeffs[0], '+', coeffs[1], '* (Slices of Beef) +',
coeffs[2], '* (Tbsp of peanut butter)')
print('\n')
rating = sandwich_regressor.evaluate_with_inputs([1, 5, 5])
print('Predicted Rating With 5,5 of peanut butter and beef:')
print(rating)
print('\n')
print('The company probably should still not trust this because')
print('while previously it was a rating of 12.5 and now its 8')
print('I can say sandwiches with both still probably do not taste good')
print(
'and that is a really high rating, the test samples that came in rated any combinations'
)
import sys
sys.path.append('src')
from dataframe import DataFrame
from polynomial_regressor import PolynomialRegressor
df = DataFrame.from_array(
[(0,1), (1,2), (2,5), (3,10), (4,20), (5,30)],
columns = ['x', 'y']
)
constant_regressor = PolynomialRegressor(degree=0)
constant_regressor.fit(df, dependent_variable='y')
print(constant_regressor.coefficients)
{'constant': 11.3333}
print(constant_regressor.predict({'x': 2}))
11.3333
linear_regressor = PolynomialRegressor(degree=1)
linear_regressor.fit(df, dependent_variable='y')
print(linear_regressor.coefficients)
{'constant': -3.2381, 'x': 5.8286}
print(linear_regressor.predict({'x': 2}))
8.4190
quadratic_regressor = PolynomialRegressor(degree=2)
quadratic_regressor.fit(df, dependent_variable='y')
print(quadratic_regressor.coefficients)
{'constant': 1.1071, 'x': -0.6893, 'x^2': 1.3036}
[1, 5, 0, 1, 0, 0, 5, 0, 0, 0, 0],\
[1, 5, 0, 0, 1, 0, 0, 5, 0, 0, 0],\
[1, 5, 0, 1, 1, 0, 5, 5, 0, 0, 1],\
[1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\
[1, 0, 5, 1, 0, 0, 0, 0, 5, 0, 0],\
[1, 0, 5, 0, 1, 0, 0, 0, 0, 5, 0],\
[1, 0, 5, 1, 1, 0, 0, 0, 5, 5, 1],\
[1, 5, 5, 0, 0, 25, 0, 0, 0, 0, 0],\
[1, 5, 5, 1, 0, 25, 5, 0, 5, 0, 0],\
[1, 5, 5, 0, 1, 25, 0, 5, 0, 5, 0],\
[1, 5, 5, 1, 1, 25, 5, 5, 5, 5, 1]])
sandwich_ratings = Matrix(elements=[[1], [1], [4], [0], [4], [8], [1], [0],
[5], [0], [9], [0], [0], [0], [0], [0]])
sandwich_regressor = PolynomialRegressor(10)
sandwich_regressor.solve_coefficients_with_inputs(sandwich_data,
sandwich_ratings)
coeffs = sandwich_regressor.coefficients
terms = ['Bias Term', 'Beef Slices', 'Peanut Butter', 'Mayo', 'Jelly', 'Beef & PB', 'Beef & Mayo', 'Beef & Jelly',\
'PB & Mayo', 'PB & Jelly', 'Mayo & Jelly']
print('\nSandwich Rating Equation')
equation_str = 'Rating ='
coeffs = [round(coeffs[i], 5) for i in range(len(coeffs))]
for i in range(len(coeffs)):
equation_str += ' '
if i > 0:
equation_str += '+ '
import sys
sys.path.append('src')
from dataframe import DataFrame
from polynomial_regressor import PolynomialRegressor
data = [(1, 3.1), (2, 10.17), (3, 20.93), (4, 38.71), (5, 60.91), (6, 98.87), (7, 113.92), (8, 146.95), (9, 190.09), (10, 232.65)]
columns = ['time', 'distance']
df = DataFrame.from_array(data, columns)
# Part A
print("\nquadratic_regressor")
quadratic_regressor = PolynomialRegressor(degree=2)
quadratic_regressor.fit(df, dependent_variable='distance')
quadratic_regressor.solve_coefficients()
n_list = [5, 10, 200]
for n in n_list:
prediction = quadratic_regressor.predict({'time': n})
print(n, "->", prediction)
# Part B
print("\ncubic_regressor")
cubic_regressor = PolynomialRegressor(degree=3)
cubic_regressor.fit(df, dependent_variable='distance')
cubic_regressor.solve_coefficients()
new_data = []
for i in range(len(data)):
new_data.append([])
for j in range(len(data[0])):
if j == len(data[0]) - 1:
results.append([math.log((10 / data[i][j]) - 1)])
else:
new_data[i].append(data[i][j])
return [new_data, results]
split = split_data(data)
sandwich_data = Matrix(elements=split[0])
sandwich_ratings = Matrix(elements=split[1])
regressor = PolynomialRegressor(2)
regressor.solve_coefficients_with_inputs(sandwich_data, sandwich_ratings)
coeffs = regressor.coefficients
def make_interactions(starters):
all_interactions = []
for i in range(len(starters)):
all_interactions.append(starters[i])
for i in range(len(starters)):
for j in range(len(starters)):
if j > i:
all_interactions.append(starters[i] * starters[j])
all_interactions.insert(0, 1)
import sys
sys.path.append('src')
from polynomial_regressor import PolynomialRegressor
from dataframe import DataFrame
data = [(1, 3.1), (2, 10.17), (3, 20.93), (4, 38.71), (5, 60.91), (6, 98.87),
(7, 113.92), (8, 146.95), (9, 190.09), (10, 232.65)]
df = DataFrame.from_array(data, ['time', 'distance'])
quadratic_regressor = PolynomialRegressor(degree=2)
quadratic_regressor.fit(df, 'distance')
print('Quadratic Regressor:')
print(quadratic_regressor.coefficients)
for t in [5, 10, 200]:
print('Distance after ' + str(t) + ' seconds:',
quadratic_regressor.predict({'time': t}))
df = DataFrame.from_array(data, ['time', 'distance'])
cubic_regressor = PolynomialRegressor(degree=3)
cubic_regressor.fit(df, 'distance')
print('Cubic Regressor:')
print(cubic_regressor.coefficients)
for t in [5, 10, 200]:
print('Distance after ' + str(t) + ' seconds:',
cubic_regressor.predict({'time': t}))
import sys
sys.path.append('src')
from polynomial_regressor import PolynomialRegressor
from matrix import Matrix
from dataframe import DataFrame
df = DataFrame.from_array([(0, 1), (1, 2), (2, 5), (3, 10), (4, 20), (5, 30)],
columns=['x', 'y'])
print('Testing degree 0 polynomial regressor...')
constant_regressor = PolynomialRegressor(degree=0)
constant_regressor.fit(df, dependent_variable='y')
assert [
round(constant_regressor.coefficients[n], 4)
for n in constant_regressor.coefficients
] == [11.3333]
assert round(constant_regressor.predict({'x': 2}), 4) == 11.3333
print('PASSED')
print('Testing degree 1 polynomial regressor...')
linear_regressor = PolynomialRegressor(degree=1)
linear_regressor.fit(df, dependent_variable='y')
assert [
round(linear_regressor.coefficients[n], 4)
for n in linear_regressor.coefficients
] == [-3.2381, 5.8286]
assert round(linear_regressor.predict({'x': 2}), 4) == 8.4190
print('PASSED')
print('Testing degree 2 polynomial regressor...')
quadratic_regressor = PolynomialRegressor(degree=2)