Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more...
A robust alternative to k-means is PAM, which is based on medoids. As discussed in the next chapter, the PAM clustering can be computed using the functionpam() [clusterpackage]. The functionpamk( ) [fpc package] is a wrapper for PAM that also prints the suggested number of clusters bas...
Quantitative thought is a technique that transforms abstract theories into concrete numerical models. This method is widely applied in various fields, including finance, engineering, and scientific research. The core of quantitative thought lies in using data analysis and model building to predict and ...
The first step when using k-means clustering is to indicate the number of clusters (k) that will be generated in the final solution. The algorithm starts by randomly selecting k objects from the data set to serve as the initial centers for the clusters. The selected objects are also ...
Python: Core programming language. ALPSQuTip: A library intended to bridge the gap between ALPS condensed matter models and QuTiP calculations. ALPS: A collection of libraries for solving models in condensed matter physics, focusing on quantum many-body systems. QuTiP: For quantum computing simulation...
Installing the Python data stack on Mac OS X and Linux Getting ready How to do it... How it works... There's more... See also Installing extra Python packages Getting ready How to do it... How it works... There's more... See also Installing and using virtualenv Getting ready How...
SparklingGraph: A Python library to process large-scale graphs using Spark and GraphX in a distributed manner OpenNE: An open-source network embedding library Galry: A high-performance visualization library in Python Dedupe: A Python library for fuzzy entity resolution and record deduplication PyText...
Computing PAM in R Data We’ll use the demo data sets “USArrests”, which we start by scaling (Chapterdata preparation and R packages) using the R functionscale()as follow: data("USArrests")# Load the data setdf <- scale(USArrests)# Scale the datahead(df, n =3)# View the firt ...
Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, the quantum resource is too scarce to support these exploration
computing can be done using the Python programming language and the computing environment that has appeared around this language. In this book the reader is assumed to have some previous training in mathematics and numerical methods and basic knowledge about Python programming. The focus of the book...