Showing results for testing Vector Vector Product
GitHub Repo
https://github.com/akash-harish2007/Vector-Operations
akash-harish2007/Vector-Operations
Python library for 2D/3D vector operations with interactive CLI. Supports addition, subtraction, dot/cross products, magnitude, normalization, and angle calculations. Includes comprehensive tests and practical examples
GitHub Repo
https://github.com/plastic-plant/vectorshop
plastic-plant/vectorshop
Generate a test web shop with household product items, images, descriptions and user reviews. Example with vector embeddings in Typesense for semantic search.
GitHub Repo
https://github.com/yunusyosaf399/test_vector_generator_for_dot_product_engine
yunusyosaf399/test_vector_generator_for_dot_product_engine
This repo contain python script to generate test vectors for dot product engine
GitHub Repo
https://github.com/dossancto/FindProducts-Vector
dossancto/FindProducts-Vector
A test for searching for products using python
GitHub Repo
https://github.com/pruggerd/Structural-Vector-Autoregression-Modeling
pruggerd/Structural-Vector-Autoregression-Modeling
I analyze the interplay of three U.S. time series: unemployment, inflation and gross domestic product. The first cleans the data and invests seasonality and stationarity. The second part develops a (structural) vector autoregressive model and test structural identification. The third uses principal compnent analysis and three different quality criterions to forecast quarterly U.S. GDP.
GitHub Repo
https://github.com/jorgeordovi/Ai_scalar_product_vectorials
jorgeordovi/Ai_scalar_product_vectorials
testing scalar products and vectorials
GitHub Repo
https://github.com/1tylermitchell/vectorwise_geo
1tylermitchell/vectorwise_geo
Testing out some ideas for handling geospatial data in Actian's Vectorwise product. Basically a node-edge topology and related concepts.
GitHub Repo
https://github.com/sahidesu25/Sentiment-Analysis-on-Amazon-Product-Reviews
sahidesu25/Sentiment-Analysis-on-Amazon-Product-Reviews
With the explosion of social networking sites, blogs and review sites a lot of information is available on the web. This information contains emotions and opinions about various product features and the makers of these products. This form of opinion and feedback is important to the companies developing these products as well as the companies that want to develop better rival products. Sentiment Analysis is the task of analyzing all this data, retrieving opinions about these products and services and classifying them as positive or negative, in other words good or bad. The key parts of any review of any product are the numeric rating and the textual description provided along with this product. In our project we will take into consideration both these vectors for product reviews to conclusively decide on a classifier that is best suited to analysis of product reviews. We have gathered reviews and based on the features that best describe the sentiment for each review, we have created a feature set of 1000 features, and with this limited set we will determine which classifier gives the best result on review type data. To determine the best classifier we perform evaluations on it, by running various data set generators, calculating the resubstitution and generalization errors for each classifier. We then use the mean of these results to compute the paired Student’s t-test to relatively compare the performance of the classifiers. Based on the results of this evaluation, we can state which is the best classifier.
GitHub Repo
https://github.com/byukan/Marketing-Data-Science