jgreen819903's starred repositories
blockchain_guide
Introduce blockchain related technologies, from theory to practice with bitcoin, ethereum and hyperledger.
python_blockchain_app
A fully functional blockchain application implemented in Python from scratch (with tutorial).
Pymol-script-repo
Collected scripts for Pymol
gmx_MMPBSA
gmx_MMPBSA is a new tool based on AMBER's MMPBSA.py aiming to perform end-state free energy calculations with GROMACS files.
computational_chemistry
Files used in TMP Chem videos on computational chemistry
python-blockchain-tutorial
Official course repository for "Python, JS, & React | Build a Blockchain & Cryptocurrency" by David Katz.
pythonineducation.org
The pythonineducation.org website
physical_chemistry
Files used in TMP Chem videos on physical chemistry
Chromatin_Analysis_2020_cell
Source codes and example scripts for chromatin tracing projects in Zhuang lab
clustering-modelsfor-ML
lustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm
Cryptocurrencies_website_payment
Simple Python script to accept cryptocurrencies payment for website owner
pdbxyz-xyzpdb
Scripts to convert between PDB to Tinker XYZ files, without using the existing TINKER programs for conversion.
MultipleLinearRegressionPython
Multiple linear regression with Python, numpy, matplotlib, plot in 3d
PyVision-The-Graph_Plotter
PyVision is a unique graph plotter which provides you the option to plot the basic line plots to complex 3d plots. This tool can even be used for statistical analysis of data with the help of bar charts, histograms and pie charts. It has the functionality of even solving the complex trigonometric and logarithmic mixed plot. You name it and we plot it.
automation-uv-vis-spectroscopy
Automating interpretation of UV/VIS CSV data and calculating/plotting relevant data
R-DataPlots
tiny R script to visualize & summarize UV spectroscopy data
SAWpyResearch
Calculate and visualize minimum energy states of self avoiding walks on a 3D cubic lattice