AW-t-SNE

The test data, code and result of the AW t-SNE algorithm

Structure of the folder

Datasets: This folder contains two datasets, the MNIST dataset and the medical record dataset.

MNIST dataset:We selected two easily misidentified digits, 4 and 9, based on the digit labels to form the new dataset(mnist_data(1000) and mnist_data(2000)):

- mnist_data(1000)：mnist_data(1000) contains a total of 1000 groups of data, each consisting of 784 data point attributes and 1 label attribute.
- mnist_data(2000)：mnist_data(2000) contains a total of 1000 groups of data, each consisting of 784 data point attributes and 1 label attribute.
- handled_mnist_data(1000)：the dataset obtained by binarising mnist_data(1000).
- handled_mnist_data(2000)：the dataset obtained by binarising mnist_data(2000).

Medical Record dataset:A total of 13 attributes are included:

- medical record_data：This is our origin medical record data, which contains 13 attributes, namely LOH, COG, AGE, TOO, SDH, LSH, CEH, TCH, CWM, COD, CLH, CCT, CCM.
- handled_medical record_data(1000)：The dataset is obtained by standardising the medical record_data and randomly selecting 1000 groups of data.
- handled_medical record_data(2000)：The dataset is obtained by standardising the medical record_data and randomly selecting 2000 groups of data.

Module：This folder contains all the codes that needs to be used.

- critic method.py：This is the code for calculating the weights of the data matrix by the critic weight method.
- svd method.py：This is the code for calculating the weights of the data matrix by the svd method.
- entrophy weight method.py：This is the code for calculating the weights of the data matrix by the entrophy weight method.
- PSO(MNIST).py：This is the code to calculate the optimal weights of the MNIST data matrix by the PSO algorithm.
- PSO(Medical Record).py：This is the code to calculate the optimal weights of the Medical Record data matrix by the PSO algorithm.
- t-SNE(MNIST).py：This is the code for the t-SNE dimensionality reduction algorithm of MNIST data.
- t-SNE(medical record).py：This is the code for the t-SNE dimensionality reduction algorithm of Medical Record data.
- AW t-SNE(Medical Record).py：This is the code for the t-SNE algorithm for dimensionality reduction of Medical Record data.
- AW t-SNE(MNIST).py：This is the code for the t-SNE algorithm for dimensionality reduction of MNIST data.

Result：This folder includes two-dimensional data points generated by the t-sne algorithm and the aw t-sne algorithm after dimensionality reduction of data in datasets

- t-SNE_mnist_data(1000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 1000 groups of binarized MNIST data using t-sne algorithm
- t-SNE_mnist_data(2000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 2000 groups of binarized MNIST data using t-sne algorithm
- aw t-SNE_mnist_data(1000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 1000 groups of binarized MNIST data using aw t-sne algorithm
- aw t-SNE_mnist_data(2000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 2000 groups of binarized MNIST data using aw t-sne algorithm
- t-SNE_medical record_data(1000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 1000 groups of binarized medical record data using t-sne algorithm
- t-SNE_medical record_data(2000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 2000 groups of binarized medical record data using t-sne algorithm
- aw t-SNE_medical record_data(1000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 1000 groups of binarized medical record data using aw t-sne algorithm
- aw t-SNE_medical record_data(2000).json：The JSON file is a two-dimensional data point set generated by reducing the dimension of 2000 groups of binarized medical record data using aw t-sne algorithm