fix: accept array of categories in classifier initialization (#111)

* fix: accept array of categories in classifier initialization

Bayes.new and LogisticRegression.new now accept either:
- Splat arguments: Bayes.new('Spam', 'Ham')
- Array argument: Bayes.new(['Spam', 'Ham'])

Both forms are now equivalent, fixing the issue where array
arguments were treated as a single category name.

Fixes #110

* docs: add visual indicators to comparison table and fix URL

Add checkmark and X emoji to the Why This Library comparison table
for better visual scanning. Fix Logistic Regression guide URL to
use correct path (logisticregression without hyphen).

Author: cardmagic

README.md

@@ -12,11 +12,11 @@ Text classification in Ruby. Five algorithms, native performance, streaming supp
 
 | | This Gem | Other Forks |
 |:--|:--|:--|
-| **Algorithms** | 5 classifiers | 2 only |
-| **Incremental LSI** | Brand's algorithm (no rebuild) | Full SVD rebuild on every add |
-| **LSI Performance** | Native C extension (5-50x faster) | Pure Ruby or requires GSL |
-| **Streaming** | Train on multi-GB datasets | Must load all data in memory |
-| **Persistence** | Pluggable (file, Redis, S3) | Marshal only |
+| **Algorithms** | ✅ 5 classifiers | ❌ 2 only |
+| **Incremental LSI** | ✅ Brand's algorithm (no rebuild) | ❌ Full SVD rebuild on every add |
+| **LSI Performance** | ✅ Native C extension (5-50x faster) | ❌ Pure Ruby or requires GSL |
+| **Streaming** | ✅ Train on multi-GB datasets | ❌ Must load all data in memory |
+| **Persistence** | ✅ Pluggable (file, Redis, S3) | ❌ Marshal only |
 
 ## Installation
 
@@ -42,7 +42,7 @@ classifier = Classifier::LogisticRegression.new(:positive, :negative)
 classifier.train(positive: "Great product!", negative: "Terrible experience")
 classifier.classify "Loved it!"  # => "Positive"
 ```
-[Logistic Regression Guide →](https://rubyclassifier.com/docs/guides/logistic-regression/basics)
+[Logistic Regression Guide →](https://rubyclassifier.com/docs/guides/logisticregression/basics)
 
 ### LSI (Latent Semantic Indexing)
 

lib/classifier/bayes.rb

@@ -26,11 +26,12 @@ class Bayes # rubocop:disable Metrics/ClassLength
     # The class can be created with one or more categories, each of which will be
     # initialized and given a training method. E.g.,
     #      b = Classifier::Bayes.new 'Interesting', 'Uninteresting', 'Spam'
-    # @rbs (*String | Symbol) -> void
+    #      b = Classifier::Bayes.new ['Interesting', 'Uninteresting', 'Spam']
+    # @rbs (*String | Symbol | Array[String | Symbol]) -> void
     def initialize(*categories)
       super()
       @categories = {}
-      categories.each { |category| @categories[category.prepare_category_name] = {} }
+      categories.flatten.each { |category| @categories[category.prepare_category_name] = {} }
       @total_words = 0
       @category_counts = Hash.new(0)
       @category_word_count = Hash.new(0)

lib/classifier/logistic_regression.rb

@@ -46,20 +46,22 @@ class LogisticRegression # rubocop:disable Metrics/ClassLength
     #
     #   classifier = Classifier::LogisticRegression.new(:spam, :ham)
     #   classifier = Classifier::LogisticRegression.new('Positive', 'Negative', 'Neutral')
+    #   classifier = Classifier::LogisticRegression.new(['Positive', 'Negative', 'Neutral'])
     #
     # Options:
     # - learning_rate: Step size for gradient descent (default: 0.1)
     # - regularization: L2 regularization strength (default: 0.01)
     # - max_iterations: Maximum training iterations (default: 100)
     # - tolerance: Convergence threshold (default: 1e-4)
     #
-    # @rbs (*String | Symbol, ?learning_rate: Float, ?regularization: Float,
+    # @rbs (*String | Symbol | Array[String | Symbol], ?learning_rate: Float, ?regularization: Float,
     #       ?max_iterations: Integer, ?tolerance: Float) -> void
     def initialize(*categories, learning_rate: DEFAULT_LEARNING_RATE,
                    regularization: DEFAULT_REGULARIZATION,
                    max_iterations: DEFAULT_MAX_ITERATIONS,
                    tolerance: DEFAULT_TOLERANCE)
       super()
+      categories = categories.flatten
       raise ArgumentError, 'At least two categories required' if categories.size < 2
 
       @categories = categories.map { |c| c.to_s.prepare_category_name }

test/bayes/bayesian_test.rb

@@ -17,6 +17,17 @@ def test_categories
     assert_equal %w[Interesting Uninteresting].sort, @classifier.categories.sort
   end
 
+  def test_array_initialization
+    classifier = Classifier::Bayes.new(%w[Spam Ham])
+
+    assert_equal %w[Ham Spam], classifier.categories.sort
+
+    classifier.train_spam 'bad nasty spam email'
+    classifier.train_ham 'good legitimate email'
+
+    assert_equal 'Spam', classifier.classify('this is spam')
+  end
+
   def test_add_category
     @classifier.add_category 'Test'
 

test/logistic_regression/logistic_regression_test.rb

@@ -19,6 +19,16 @@ def test_accepts_symbols_and_strings
     assert_equal %w[Spam Ham].sort, classifier2.categories.sort
   end
 
+  def test_accepts_array_of_categories
+    classifier = Classifier::LogisticRegression.new(%w[Spam Ham])
+
+    assert_equal %w[Ham Spam], classifier.categories.sort
+  end
+
+  def test_array_initialization_requires_at_least_two
+    assert_raises(ArgumentError) { Classifier::LogisticRegression.new(['Only']) }
+  end
+
   def test_custom_hyperparameters
     classifier = Classifier::LogisticRegression.new(
       :spam, :ham,