Estimate Params for linear model

[teubert]

Requirement Text
Estimate the parameters for a linear model from data

Background Information
Linear Models exist but the ability to estimate those parameters from data (i.e., regression) isn't supported

Suggested Solution
Subclass data model, implement from_data to estimate matrices

DoD

• Implement feature
• Add to example
• Add tests
• update documentation
• Add to change notes for next release

[suryasekhar06jemsbond-lab]

Implement a Method to Estimate Parameters

progpy/models/linear_model.py

import numpy as np

class LinearModel:
def init(self, coefficients=None):
self.coefficients = coefficients

@classmethod
def from_data(cls, X, y):
    """
    Estimate the parameters (coefficients) for a linear model using least squares.
    """
    X = np.c_[np.ones(X.shape[0]), X]  # Add intercept term
    coefficients = np.linalg.inv(X.T @ X) @ X.T @ y
    return cls(coefficients)

2. Explanation

What this does: Adds a class method from_data to the LinearModel class that estimates the model parameters using the least squares method.

Why this fixes it: Provides functionality to estimate model parameters directly from data, enhancing the model's usability.

Next steps: Integrate this method into the relevant parts of the codebase and update documentation accordingly.

3. Git Commands

git checkout -b feat/linear-model-parameter-estimation
mkdir -p progpy/models

Create progpy/models/linear_model.py and add the code above

git add progpy/models/linear_model.py
git commit -m "Implement parameter estimation for linear model from data"
git push origin feat/linear-model-parameter-estimation

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