Regression
Regression Models
Linear Regression
Module: deeprai.models.regression.linear_regression
This module introduces a simple Linear Regression model. Linear Regression is a statistical technique commonly used for modeling and analyzing relationships between two variables.
Class: LinearRegression
A class representation of the linear regression model.
1. Initializer: __init__(self)
Description:
Initializes the LinearRegression class.
Attributes:
- fitted_vals (
list): A list to store the results after the model has been fitted. This is primarily the coefficients of the linear equation.
Example:
from deeprai.models.regression.linear_regression import LinearRegression
model = LinearRegression()
2. Method: fit(self, x_vals, y_vals)
Description:
Fit the model to the given x_vals and y_vals using linear regression.
Parameters:
-
x_vals (
listornp.ndarray): The input values or features. -
y_vals (
listornp.ndarray): The output values or labels.
Returns:
list: Coefficients of the linear equation.
Example:
model.fit(x_vals=[1, 2, 3], y_vals=[2, 4, 6])
3. Method: run(self, x_val)
Description:
Use the previously fitted model to predict the output for a given x_val.
Parameters:
- x_val (
float): The input value for which the prediction is desired.
Returns:
float: Predicted value based on the linear regression equation.
Example:
predicted_val = model.run(4)
print(predicted_val)Poly Regression
Module: deeprai.models.regression.poly_regression
Class: PolyRegression
A class representation of the polynomial regression model.
1. Initializer: __init__(self)
Description:
Initializes the PolyRegression class.
Attributes:
- fitted_vals (
list): A list to store the results after the model has been fitted. These values represent the coefficients of the polynomial equation.
Example:
from deeprai.models.regression import PolyRegression
model = PolyRegression()
2. Method: fit(self, x_vals, y_vals)
Description:
Fit the model to the given x_vals and y_vals using polynomial regression.
Parameters:
-
x_vals (
listornp.ndarray): The input values or features. -
y_vals (
listornp.ndarray): The output values or labels.
Returns:
list: Coefficients of the polynomial equation, starting from the coefficient of the highest degree term.
Example:
model.fit(x_vals=[1, 2, 3], y_vals=[2, 5, 10])
3. Method: run(self, x_val)
Description:
Use the previously fitted model to predict the output for a given x_val based on the polynomial equation.
Parameters:
- x_val (
float): The input value for which the prediction is desired.
Returns:
float: Predicted value based on the polynomial regression equation.
Example:
predicted_val = model.run(4)
print(predicted_val)Sine Regression
Module: deeprai.models.regression.sine_regression
Class: SineRegression
A class representation of the sine regression model.
1. Initializer: __init__(self)
Description:
Initializes the SineRegression class.
Attributes:
- fitted_vals (
list): A list to store the results after the model has been fitted. These values represent the parameters of the sine equation.
Example:
from deeprai.models.regression import SineRegression
model = SineRegression()
2. Method: fit(self, x_vals, y_vals)
Description:
Fit the model to the given x_vals and y_vals using sine regression.
Parameters:
-
x_vals (
listornp.ndarray): The input values or features. -
y_vals (
listornp.ndarray): The output values or labels.
Returns:
list: Parameters of the sine equation, which includes amplitude, frequency, phase shift, and vertical shift.
Example:
model.fit(x_vals=[1, 2, 3], y_vals=[2, 1.5, 2.5])
3. Method: run(self, x_val)
Description:
Use the previously fitted model to predict the output for a given x_val based on the sine equation.
Parameters:
- x_val (
float): The input value for which the prediction is desired.
Returns:
float: Predicted value based on the sine regression equation.
Example:
predicted_val = model.run(4)
print(predicted_val)