Consecutive-Subsequence - Simple software to calculate susequence with highest sum

Overview

Simple software to calculate susequence with highest sum

This repository contains code and associated files for calculating the consecutive subsequence with the highest sum.This solution runs in the O(nLogn) which is mostly the time complexity of the sorting operation. Other operations are neglible as n approaches infinity. References: geeksforgeeks

Background/Introduction

Nowadays, IOT systems collect an enormous amount of sensory data every second. To make use of this large pool of sensory data in a pipeline, you might need to identify interesting subsequences. The project is an abstraction of this, with the list of integers representing the sensor data and the sum is the metric to evaluate if a subsequence is interesting. The goal of this project is to enable users to quickly obtain the highest sum from a list of integers.

High Level Flow

  • User Input: python find_subsequence.py source_data max_subsequence_length

    • source_data (mandatory): Specifies the file path where the data is stored. A text file containing integers is the expected format.
    • max_subsequence_length (optional): Specifies the maximum possible subsequence length that can be considered.
  • Output: Highest sum as an integer

Steps to run the code

  • git clone this repository at https://github.com/pharouhk/Consecutive-Subsequence.git to quickly get the source code and all other dependencies.
  • You will see the following file structure below:
    • pycahce
    • sensordata
      • input_1.txt
      • input_2.txt
      • input_3.txt
    • READme.md
    • find_subsequence.py
    • requirements.txt
    • unittest.py
  • Open the command line and run the following command. The run command requires a file path to be passed as an argument and an optional numeric argument.
    cd folder_name/Consecutive Subsequence
    pip3 install -r requirements.txt
    python find_subsequence.py sensordata/input_1.txt 2
    
  • Your result would be displayed in the stdout in the command line
  • The unit tests can be run using the following command
    python
    import unittest as u
    u.test_subsequence()
    
  • You should get "Tests Passed!" output
Owner
Gbadamosi Farouk
Data Scientist and Machine Learning Engineer.
Gbadamosi Farouk
a curated list of docker-compose files prepared for testing data engineering tools, databases and open source libraries.

data-services A repository for storing various Data Engineering docker-compose files in one place. How to use it ? Set the required settings in .env f

BigData.IR 525 Dec 03, 2022
Code for Fold2Seq paper from ICML 2021

[ICML2021] Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design Environment file: environment.yml Data and Feat

International Business Machines 43 Dec 04, 2022
HINet: Half Instance Normalization Network for Image Restoration

HINet: Half Instance Normalization Network for Image Restoration Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, Chengpeng Chen Paper: https://arxiv.org

303 Dec 31, 2022
Object-aware Contrastive Learning for Debiased Scene Representation

Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo

43 Dec 14, 2022
MAGMA - a GPT-style multimodal model that can understand any combination of images and language

MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning Authors repo (alphabetical) Constantin (CoEich), Mayukh (Mayukh

Aleph Alpha GmbH 331 Jan 03, 2023
A data-driven maritime port simulator

PySeidon - A Data-Driven Maritime Port Simulator 🌊 Extendable and modular software for maritime port simulation. This software uses entity-component

6 Apr 10, 2022
Colar: Effective and Efficient Online Action Detection by Consulting Exemplars, CVPR 2022.

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars This repository is the official implementation of Colar. In this work,

LeYang 246 Dec 13, 2022
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks

LMMNN Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks This is the working dire

Giora Simchoni 10 Nov 02, 2022
natural image generation using ConvNets

The Eyescream Project Generating Natural Images using Neural Networks. For our research summary on this work, please read the Arxiv paper: http://arxi

Meta Archive 601 Nov 23, 2022
Official implementation of the NeurIPS'21 paper 'Conditional Generation Using Polynomial Expansions'.

Conditional Generation Using Polynomial Expansions Official implementation of the conditional image generation experiments as described on the NeurIPS

Grigoris 4 Aug 07, 2022
This program will stylize your photos with fast neural style transfer.

Neural Style Transfer (NST) Using TensorFlow Demo TensorFlow TensorFlow is an end-to-end open source platform for machine learning. It has a comprehen

Ismail Boularbah 1 Aug 08, 2022
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

An-Introduction-to-Statistical-Learning This repository contains the exercises and its solution contained in the book An Introduction to Statistical L

2.1k Jan 02, 2023
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

Kentaro Wada 218 Oct 27, 2022
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

NonCuboidRoom Paper Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image Cheng Yang*, Jia Zheng*, Xili Dai, Rui Tang, Yi Ma, Xiao

67 Dec 15, 2022
Source code for the BMVC-2021 paper "SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation".

SimReg: A Simple Regression Based Framework for Self-supervised Knowledge Distillation Source code for the paper "SimReg: Regression as a Simple Yet E

9 Oct 15, 2022
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model

Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model About This repository contains the code to replicate the syn

Haruka Kiyohara 12 Dec 07, 2022
A Python library for generating new text from existing samples.

ReMarkov is a Python library for generating text from existing samples using Markov chains. You can use it to customize all sorts of writing from birt

8 May 17, 2022
50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program

50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

komal_lamba 22 Dec 09, 2022
Official code of paper: MovingFashion: a Benchmark for the Video-to-Shop Challenge

SEAM Match-RCNN Official code of MovingFashion: a Benchmark for the Video-to-Shop Challenge paper Installation Requirements: Pytorch 1.5.1 or more rec

HumaticsLAB 31 Oct 10, 2022
TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL, and utterance id

TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL

3 Dec 26, 2022