pec-css / slowmokit Goto Github PK

Overload operator+ operator- and operator *. Function signatures are present in matrix.h already at Line-145, 146, 155 and 156.
Also overload operator += and operator -= for a matrix and scalar (Function signature for this is not present so add that in matrix.h too.)

Feature: Logistic regression model

Implement a logistic regression model

Automate the creation of all files needed to add a model

Feature : Implement Random Forest for regression

Feature: Implement train test split for pre-processing

Implement mini-batch gradient descent

Feature: Implement Gaussian Naive Bayes

Feature : Implement Agglomerative Clustering

Feature: Implement SVM for Classification

Feature : Implement Complement Naive Bayes

Feature : Implement a basic neural network architecture

Implement Label Encoder for preprocessing

Feature : Implement Categorical Naive Bayes

Fix CMake to make all the examples running

Implement standardization for data pre-processing.

Implement accuracy

Feature: Implement DBSCAN clustering algorithm

Feature : Implement Various Activation Functions

Sigmoid, ReLU, tanh, tan-1, softmax, binary step, Leaky ReLU

Implement Mean-Squared Error

Implement matmul, subtract, add function.

Add function signature for each function inside the Matrix class in matrix.hpp and also implement in matrix.cpp.
Function specifications:
- matmul: Multiplies two Matrices. A Matrix and a scalar is not allowed. Also throw appropriate exception if matrix dimension are not compatible for multiplication.
- add: This function will have two overloads one which will add matrix and scalar and the other one for two matrices. Throw appropriate exceptions if matrix have incompatible dimensions.
- subtract: Everything same as add function but you should do subtraction instead of addition.
Don't implement from scratch, use the already implemented functions operator +=, operator -= and operator *=. This way you will not have to worry about throwing appropriate exceptions as the already implemented function will do it for you.
Don't forget to add comments above each function signature you add in matrix.hpp. Avoid adding comments in matrix.cpp unless very important.

Implement precision and recall for a multiclass classifier

N-matrix declaration

Declare a N dimensional matrix class.

constructors

Feature: Implement the code to plot a dendogram

The dendogram should give different colours to different clusters based on the Agglomerative Clustering as the below image shows that there should be 2 clusters in the dataset

KNN classifier

KNN classifier in c++

Implement Silhouette Score

Overloading [] and << operator

Easy Category
Accessing matrix like vector and printing it as we need will make it easier for people to use it.

K means clustering

Feature: Implement Ridge and Lasso regression

Feature: Implement Bernoulli Naive Bayes

Implement confusion matrix (with heatmap) for multi-class classifier

Feature: Implement Multinomial Naive Bayes

Cleanup metrics directory

Create a metrics class in metrics dir and add all 5 functions as static functions.
Test all function's working before making a PR

pec-css / slowmokit Goto Github PK

slowmokit's Issues

Recommend Projects

Recommend Topics

Recommend Org