Merge pull request #43 from cardmagic/refactor/early-returns

Refactor nested conditionals to early returns

Author: cardmagic

CLAUDE.md

@@ -0,0 +1,67 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Ruby gem providing text classification via two algorithms:
+- **Bayes** (`Classifier::Bayes`) - Naive Bayesian classification
+- **LSI** (`Classifier::LSI`) - Latent Semantic Indexing for semantic classification, clustering, and search
+
+## Common Commands
+
+```bash
+# Run all tests
+rake test
+
+# Run a single test file
+ruby -Ilib test/bayes/bayesian_test.rb
+ruby -Ilib test/lsi/lsi_test.rb
+
+# Run tests with native Ruby vector (without GSL)
+NATIVE_VECTOR=true rake test
+
+# Interactive console
+rake console
+
+# Generate documentation
+rake doc
+```
+
+## Architecture
+
+### Core Components
+
+**Bayesian Classifier** (`lib/classifier/bayes.rb`)
+- Train with `train(category, text)` or dynamic methods like `train_spam(text)`
+- Classify with `classify(text)` returning the best category
+- Uses log probabilities for numerical stability
+
+**LSI Classifier** (`lib/classifier/lsi.rb`)
+- Uses Singular Value Decomposition (SVD) for semantic analysis
+- Optional GSL gem for 10x faster matrix operations; falls back to pure Ruby SVD
+- Key operations: `add_item`, `classify`, `find_related`, `search`
+- `auto_rebuild` option controls automatic index rebuilding after changes
+
+**String Extensions** (`lib/classifier/extensions/word_hash.rb`)
+- `word_hash` / `clean_word_hash` - tokenize text to stemmed word frequencies
+- `CORPUS_SKIP_WORDS` - stopwords filtered during tokenization
+- Uses `fast-stemmer` gem for Porter stemming
+
+**Vector Extensions** (`lib/classifier/extensions/vector.rb`)
+- Pure Ruby SVD implementation (`Matrix#SV_decomp`)
+- Vector normalization and magnitude calculations
+
+### GSL Integration
+
+LSI checks for the `gsl` gem at load time. When available:
+- Uses `GSL::Matrix` and `GSL::Vector` for faster operations
+- Serialization handled via `vector_serialize.rb`
+- Test without GSL: `NATIVE_VECTOR=true rake test`
+
+### Content Nodes (`lib/classifier/lsi/content_node.rb`)
+
+Internal data structure storing:
+- `word_hash` - term frequencies
+- `raw_vector` / `raw_norm` - initial vector representation
+- `lsi_vector` / `lsi_norm` - reduced dimensionality representation after SVD

Gemfile.lock

@@ -3,7 +3,6 @@ PATH
   specs:
     classifier (1.4.4)
       fast-stemmer (~> 1.0)
-      matrix
       mutex_m (~> 0.2)
       rake
 
@@ -24,7 +23,7 @@ GEM
 PLATFORMS
   arm64-darwin-22
   arm64-darwin-23
-  arm64-darwin-24
+  arm64-darwin-25
   x86_64-linux
 
 DEPENDENCIES

lib/classifier/bayes.rb

@@ -100,20 +100,13 @@ def classify(text)
     #     b.untrain_that "That text"
     #     b.train_the_other "The other text"
     def method_missing(name, *args)
+      return super unless name.to_s =~ /(un)?train_(\w+)/
+
       category = name.to_s.gsub(/(un)?train_(\w+)/, '\2').prepare_category_name
-      if @categories.key?(category)
-        args.each do |text|
-          if name.to_s.start_with?('untrain_')
-            untrain(category, text)
-          else
-            train(category, text)
-          end
-        end
-      elsif name.to_s =~ /(un)?train_(\w+)/
-        raise StandardError, "No such category: #{category}"
-      else
-        super
-      end
+      raise StandardError, "No such category: #{category}" unless @categories.key?(category)
+
+      method = name.to_s.start_with?('untrain_') ? :untrain : :train
+      args.each { |text| send(method, category, text) }
     end
 
     #

lib/classifier/extensions/vector.rb

@@ -8,12 +8,9 @@
 class Array
   def sum_with_identity(identity = 0.0, &block)
     return identity unless size.to_i.positive?
+    return map(&block).sum_with_identity(identity) if block_given?
 
-    if block_given?
-      map(&block).sum_with_identity(identity)
-    else
-      compact.reduce(:+).to_f || identity.to_f
-    end
+    compact.reduce(:+).to_f || identity.to_f
   end
 end
 

lib/classifier/lsi.rb

@@ -332,13 +332,8 @@ def node_for_content(item, &block)
       return @items[item] if @items[item]
 
       clean_word_hash = block ? block.call(item).clean_word_hash : item.to_s.clean_word_hash
-
-      cn = ContentNode.new(clean_word_hash, &block) # make the node and extract the data
-
-      unless needs_rebuild?
-        cn.raw_vector_with(@word_list) # make the lsi raw and norm vectors
-      end
-
+      cn = ContentNode.new(clean_word_hash, &block)
+      cn.raw_vector_with(@word_list) unless needs_rebuild?
       cn
     end