coherence
NumPy
coherence | NumPy | |
---|---|---|
10 | 272 | |
413 | 26,360 | |
0.5% | 0.9% | |
9.7 | 10.0 | |
6 days ago | 6 days ago | |
Java | Python | |
Universal Permissive License v1.0 | GNU General Public License v3.0 or later |
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coherence
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Creating a compiler in Java
There are a few different tool-sets for producing Java byte code. I'm not sure which one to suggest, because back when I last needed one (end of '96), there were none, so I wrote my own. But I assume that most people use ASM or something similar.
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Microfeatures I'd like to see in more languages
This is one that I like a lot. Years ago (1997 timeframe) I had implemented it in a Java compiler, and a few years later in a Java library (https://github.com/oracle/coherence/blob/4e6e343e1ffd9bbfea3...) that would create an exception on the assertion failure and parse its stack trace to find the source code file name, and read it to find the text of the assertion that failed, etc. so it could build the error message ...
In Ecstasy, we built the support directly into the compiler again:
```
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What's going on behind type type declaration?
For the debugger (but not required by the runtime), there is an optional table that points to the ranges of ops at which names and types are bound to registers
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Resources to understand code generation from AST?
FWIW - here's an AST for Java that directly emits Java byte code: https://github.com/oracle/coherence/tree/master/prj/coherence-core/src/main/java/com/tangosol/dev/compiler/java
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Why text only.
It has been "experimented with" many times. Here's an example from TDE, a component-based development environment from Tangosol (now part of Oracle).
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Anybody have tips for writing a Recursive Descent Parser for an AST? [ JS ]
If it helps, here's a Java recursive descent parser that I wrote years ago.
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A new kind of scope?
If you want to see an example, here's a Context interface from a multi-language compiler framework (compiling multiple different languages to Java byte-code) that I wrote years ago.
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Are Functional Programming Languages the best option for Crafting a Compiler?
I built an entire Java compiler in four months, from scratch, by myself, over twenty years ago. (Now owned by Oracle; still used today. Thank you, Larry.) But starting from a well written spec for a simple language like Java is orders of magnitude easier than developing a new language, runtime model, and tool-chain from scratch.
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How to build an AST with a list of Tokens? (Recursive Descent)
As mentioned, the various parsing methods each contribute back an AST node, so on the way down the recursion, they are parsing, and on the way back up from the recursion, they are building the tree. Here's a fairly simple recursive descent Java compiler written in Java that I wrote a few years back, in case you are looking for an example.
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Do these examples belong to syntax or semantics and are they handled by syntactic or semantic analysis?
If you're curious how some of this can be implemented in a Java compiler, I wrote one years ago. For example, checking that the left side is an l-value:
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
Hazelcast - Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
SymPy - A computer algebra system written in pure Python
hazelcast-nodejs-client - Hazelcast Node.js Client
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
hazelcast-python-client - Hazelcast Python Client
blaze - NumPy and Pandas interface to Big Data
hazelcast-go-client - Hazelcast Go Client
SciPy - SciPy library main repository
ANTLR - ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files.
Numba - NumPy aware dynamic Python compiler using LLVM
grammars-v4 - Grammars written for ANTLR v4; expectation that the grammars are free of actions.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).