# josh environment
import sys
sys.path.insert(0, '/home/utkarsh/projects/linear_and_logistic_regression/implementation')
import multivariate_linear_regression as mlp
sys.path.insert(0, '/home/utkarsh/projects/linear_and_logistic_regression/data_tools')
import get_one_weather_vector as gv
import binary_logistic_reg as binary_log_reg

# jorge environment
# import data_tools.get_one_weather_vector as gv
# import implementation.binary_logistic_reg as binary_log_reg
# import implementation.multivariate_linear_regression as mlp

# common
import numpy as np

print("Welcome to our machine. At the current moment in time, the forecast in beijing is: ")
print(gv.now_weather_readable())

xs = np.array(gv.raw_on())[:, 0:11]
print(xs)
# print("hi")
print ("Given this data, our multivariate_linear_regression algorithm predicts that the pollution is:")
print (mlp.g.predict(xs))

print("\n=======================================================================================\n")
print("Our binary logistic regression algorithm, taking wind speed as its predictor, generates the following theta values based on historical data: ")
regressionAlgorithm = binary_log_reg.BinaryLogisticRegression()
prediction = regressionAlgorithm.predict()
print("theta 0: " + str(prediction[0]))
print("theta 1: " + str(prediction[1]))
print("\n")

# print("The actual pollution levels at that speed is: (Tweeter feed)")
# forecast.gen_entire_month("Beijing-no-mb-with-date-data", 5, 2015, should_print_headers=False)

0f4eab2614ace50fb649e25e544e196551385cf8
<mask token>

<mask token>
sys.path.insert(0,
    '/home/utkarsh/projects/linear_and_logistic_regression/implementation')
<mask token>
sys.path.insert(0,
    '/home/utkarsh/projects/linear_and_logistic_regression/data_tools')
<mask token>
print(
    'Welcome to our machine. At the current moment in time, the forecast in beijing is: '
    )
print(gv.now_weather_readable())
<mask token>
print(xs)
print(
    'Given this data, our multivariate_linear_regression algorithm predicts that the pollution is:'
    )
print(mlp.g.predict(xs))
print(
    """
=======================================================================================
"""
    )
print(
    'Our binary logistic regression algorithm, taking wind speed as its predictor, generates the following theta values based on historical data: '
    )
<mask token>
print('theta 0: ' + str(prediction[0]))
print('theta 1: ' + str(prediction[1]))
print('\n')

<mask token>
sys.path.insert(0,
    '/home/utkarsh/projects/linear_and_logistic_regression/implementation')
<mask token>
sys.path.insert(0,
    '/home/utkarsh/projects/linear_and_logistic_regression/data_tools')
<mask token>
print(
    'Welcome to our machine. At the current moment in time, the forecast in beijing is: '
    )
print(gv.now_weather_readable())
xs = np.array(gv.raw_on())[:, 0:11]
print(xs)
print(
    'Given this data, our multivariate_linear_regression algorithm predicts that the pollution is:'
    )
print(mlp.g.predict(xs))
print(
    """
=======================================================================================
"""
    )
print(
    'Our binary logistic regression algorithm, taking wind speed as its predictor, generates the following theta values based on historical data: '
    )
regressionAlgorithm = binary_log_reg.BinaryLogisticRegression()
prediction = regressionAlgorithm.predict()
print('theta 0: ' + str(prediction[0]))
print('theta 1: ' + str(prediction[1]))
print('\n')

import sys
sys.path.insert(0,
    '/home/utkarsh/projects/linear_and_logistic_regression/implementation')
import multivariate_linear_regression as mlp
sys.path.insert(0,
    '/home/utkarsh/projects/linear_and_logistic_regression/data_tools')
import get_one_weather_vector as gv
import binary_logistic_reg as binary_log_reg
import numpy as np
print(
    'Welcome to our machine. At the current moment in time, the forecast in beijing is: '
    )
print(gv.now_weather_readable())
xs = np.array(gv.raw_on())[:, 0:11]
print(xs)
print(
    'Given this data, our multivariate_linear_regression algorithm predicts that the pollution is:'
    )
print(mlp.g.predict(xs))
print(
    """
=======================================================================================
"""
    )
print(
    'Our binary logistic regression algorithm, taking wind speed as its predictor, generates the following theta values based on historical data: '
    )
regressionAlgorithm = binary_log_reg.BinaryLogisticRegression()
prediction = regressionAlgorithm.predict()
print('theta 0: ' + str(prediction[0]))
print('theta 1: ' + str(prediction[1]))
print('\n')

# josh environment
import sys
sys.path.insert(0, '/home/utkarsh/projects/linear_and_logistic_regression/implementation')
import multivariate_linear_regression as mlp
sys.path.insert(0, '/home/utkarsh/projects/linear_and_logistic_regression/data_tools')
import get_one_weather_vector as gv
import binary_logistic_reg as binary_log_reg

# jorge environment
# import data_tools.get_one_weather_vector as gv
# import implementation.binary_logistic_reg as binary_log_reg
# import implementation.multivariate_linear_regression as mlp

# common
import numpy as np

print("Welcome to our machine. At the current moment in time, the forecast in beijing is: ")
print(gv.now_weather_readable())

xs = np.array(gv.raw_on())[:, 0:11]
print(xs)
# print("hi")
print ("Given this data, our multivariate_linear_regression algorithm predicts that the pollution is:")
print (mlp.g.predict(xs))

print("\n=======================================================================================\n")
print("Our binary logistic regression algorithm, taking wind speed as its predictor, generates the following theta values based on historical data: ")
regressionAlgorithm = binary_log_reg.BinaryLogisticRegression()
prediction = regressionAlgorithm.predict()
print("theta 0: " + str(prediction[0]))
print("theta 1: " + str(prediction[1]))
print("\n")

# print("The actual pollution levels at that speed is: (Tweeter feed)")
# forecast.gen_entire_month("Beijing-no-mb-with-date-data", 5, 2015, should_print_headers=False)
