Skip to main content

Error : .onLoad failed in loadNamespace() for 'rJava', details:

Getting following error during

> library(rJava)

Error : .onLoad failed in loadNamespace() for 'rJava', details:
  call: fun(libname, pkgname)
  error: No CurrentVersion entry in Software/JavaSoft registry! Try re-installing Java and make sure R and Java have matching architectures.
Error: package or namespace load failed for ‘rJava’

The architecture of Java you get for install from the regular Java download web page depends on the architecture of browser you’re using (32-bit or 64-bit) when you download it. This happens without any fanfare. We need the Java manual download page, select the 64-bit version of Java for your OS, install it, and then (after restarting R) try your R packages again.

*** Make sure we need 64 bit Java version to download

WHY? R is 64 bit

Source: https://java.com/en/download/manual.jsp#sol

> library(rJava)

Error : .onLoad failed in loadNamespace() for 'rJava', details:
  call: inDL(x, as.logical(local), as.logical(now), ...)
  error: unable to load shared object 'C:/Users/amajumde/Documents/R/win-library/3.2/rJava/libs/x64/rJava.dll':
  LoadLibrary failure:  The specified module could not be found.

This time it's different problem. 

Check Java version: 

java -d64 -version

java version "1.8.0_91"
Java(TM) SE Runtime Environment (build 1.8.0_91-b15)
Java HotSpot(TM) 64-Bit Server VM (build 25.91-b15, mixed mode)

We need to set the Java Home to get this change effective:

> options(java.home="C:\Program Files\Java\jre1.8.0_91")

Error: '\P' is an unrecognized escape in character string starting ""C:\P"

> options(java.home="C:\\Program Files\\Java\\jre1.8.0_91")

> library(rJava)

Executed successfully. 




Comments

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.

Feature Engineering - What and Why

Feature engineering is a crucial step in the machine learning pipeline where you create new, meaningful features or transform existing features to improve the performance of your predictive models. It involves selecting, modifying, or creating features from your raw data to make it more suitable for machine learning algorithms. Here's a more detailed overview of feature engineering: Why Feature Engineering? Feature engineering is essential for several reasons: Improving Model Performance: Well-engineered features can significantly boost the predictive power of your machine learning models. Handling Raw Data: Raw data often contains noise, missing values, and irrelevant information. Feature engineering helps in cleaning and preparing the data for analysis. Capturing Domain Knowledge: Domain-specific insights can be incorporated into feature creation to make the model more representative of the problem. Common Techniques and Strategies: 1. Feature Extraction: Transforming raw data...