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.

CERN@School IRIS

Models

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. Tensorflow

The benchmark_classifiers folder contains all the classifiers used for testing against the neural network.

LCA API

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"))

Dependencies

- Tensorflow
- LUCID Utils
- Numpy