LUCID Classifiers Analysis
This analysis program was made so that various institutes such as CERN@School and the Institute for Research in Schools can use this program for analysing their data from Timepix or Medipix particle detectors.
The neural_model folder contains the neural model used for classification. The neural model can be viewed with Tensorboard with its respective accuracy and loss graphs.
The benchmark_classifiers folder contains all the classifiers used for testing against the neural network.
from lucid_classifiers.analysis import classify blob = [[0,0],[0,1],[1,0],[1,1],[0,2],[1,2],[0,3],[1,3]] ## Composite Classifier (No parameter) <- Picks the most popular prediction from all the analysis methods print(classify(blob)) ## SVM Classifier print(classify(blob,"svm")) ## KNN Classifier print(classify(blob,"knn")) ## Decision Tree Classifier print(classify(blob, "dt")) ## Random Forest Classifier print(classify(blob, "rf")) ## Neural Classifier print(classify(blob, "neural")) ## LUCID Algorithm print(classify(blob, "lucid"))
- Tensorflow - LUCID Utils - Numpy