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Module: deeprai.tools.noise This module provides a set of classes for introducing different types of noise into numpy arrays, typically used for image data augmentation or robustness testing. 1. GaussianNoise Class Description: The GaussianNoise class ap...

Module: deeprai.tools.toolkit This module provides a collection of utility functions designed for numpy arrays. These functions offer various operations like verification, rounding, normalization, reshaping, and others, enhancing usability and information ret...

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 ...

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 ...

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 t...

Function Signature def save(self, file_location: str) -> None: Parameters file_location (str): The location/path where the model's state will be saved. Return Value This function does not return anything. It saves the model's state to the specif...

Function Signature def load(self, file_location: str) -> None: Parameters file_location (str): The location/path from which the model's state will be loaded. Return Value This function does not return anything. It loads the model's state from th...

Instant Classifier Function Function Signature def instant_classifier(self, x_vals, y_vals, query_point, p=3, k=2): Parameters x_vals: The input data points. y_vals (must be converted to int32): The labels corresponding to the input data poi...

Storing Values in KNN Classifier Function Signature def store_vals(self, x_values, y_values, p=3, k=2): Parameters x_values: The input data points to be stored in the classifier. y_values (must be converted to int32): The labels corresponding ...

Classifying a Query Point with KNN Function Signature def classify(self, query_point): Parameters query_point: The point for which classification is to be determined. Return Value Returns the classification result for the query_point based on the...

Calculating Classification Probability with KNN Function Signature def classify_probability(self, query_point, expected_val): Parameters query_point: The point for which classification probability is to be determined. expected_val: The label v...

Retrieving Nearest Neighbors with KNN Function Signature def classify_neighbors(self, query_point): Parameters query_point: The point for which the nearest neighbors are to be determined. Return Value Returns the indices of the k nearest neighbor...

Configuring Distance Metric for KNN Function Signature def config_distance(self, distance): Parameters distance: The name of the distance metric to be configured for the KNN instance. Return Value This function does not return anything. It modifi...