aimacode/aima-python

23.7

Adjusted Score

23.7

Raw Score

100%

Time Factor

2026-06-30

Last Push

8.8K

Stars

Jupyter Notebook

Language

49.7K

Lines of Code

130

Files

1.1K

Pattern Hits

2026-07-14

Scan Date

0.15

HC Hit Rate

What These Metrics Mean

Adjusted Score: Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score: The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor: The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits: Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate: High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files: Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 19MEDIUM 58LOW 999

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 1076 distinct pattern matches across 18 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Over-Commented Block621 hits · 540 pts

Severity	File	Line	Snippet	Context
LOW	aima/perception.py	501	# show the demo of rcnn, the model is from	COMMENT
LOW	notebooks/bootstrap.py	1	# ---	COMMENT
LOW	notebooks/knowledge_current_best.py	1	# ---	COMMENT
LOW	notebooks/knowledge_current_best.py	21	# The [knowledge](https://github.com/aimacode/aima-python/blob/master/knowledge.py) module covers **Chapter 19: Knowledg	COMMENT
LOW	notebooks/knowledge_current_best.py	41	# ### First-Order Logic	COMMENT
LOW	notebooks/knowledge_current_best.py	61	# We say that an example `e` is consistent with an hypothesis `h` if the assignment from the hypothesis for `e` is t	COMMENT
LOW	notebooks/knowledge_current_best.py	81	# %%	COMMENT
LOW	notebooks/knowledge_current_best.py	101	#	COMMENT
LOW	notebooks/knowledge_current_best.py	141		COMMENT
LOW	notebooks/knowledge_current_best.py	161	# * How much does it cost (`Price`, takes values in [$, $$, $$$])	COMMENT
LOW	notebooks/dynamic_decision_network.py	1	# ---	COMMENT
LOW	notebooks/improving_sat_algorithms.py	1	# ---	COMMENT
LOW	notebooks/improving_sat_algorithms.py	21	# # Improving Boolean Satisfiability Algorithms	COMMENT
LOW	notebooks/improving_sat_algorithms.py	41	#	COMMENT
LOW	notebooks/improving_sat_algorithms.py	61		COMMENT
LOW	notebooks/improving_sat_algorithms.py	81	# *Freeman’s POSIT* <a name="ref-1"/>[[1]](#cite-freeman1995improvements) version counts both the number of positive	COMMENT
LOW	notebooks/improving_sat_algorithms.py	101	# %psource dlis	COMMENT
LOW	notebooks/improving_sat_algorithms.py	121	# %%	COMMENT
LOW	notebooks/improving_sat_algorithms.py	141	# The first difference between a DPLL solver and a CDCL solver is the introduction of the *non-chronological backtrackin	COMMENT
LOW	notebooks/improving_sat_algorithms.py	161		COMMENT
LOW	notebooks/improving_sat_algorithms.py	181		COMMENT
LOW	notebooks/improving_sat_algorithms.py	201	# #### Luby	COMMENT
LOW	notebooks/improving_sat_algorithms.py	221	# %psource luby	COMMENT
LOW	notebooks/improving_sat_algorithms.py	241	# ### Australia	COMMENT
LOW	notebooks/improving_sat_algorithms.py	261		COMMENT
LOW	notebooks/improving_sat_algorithms.py	281	# %time model = dpll_satisfiable(australia_sat, branching_heuristic=dlis)	COMMENT
LOW	notebooks/improving_sat_algorithms.py	341	# %%	COMMENT
LOW	notebooks/improving_sat_algorithms.py	361		COMMENT
LOW	notebooks/improving_sat_algorithms.py	421	# %time model = dpll_satisfiable(usa_sat, branching_heuristic=moms)	COMMENT
LOW	notebooks/improving_sat_algorithms.py	441	# %%	COMMENT
LOW	notebooks/improving_sat_algorithms.py	481	# %% [markdown]	COMMENT
LOW	notebooks/improving_sat_algorithms.py	501		COMMENT
LOW	notebooks/improving_sat_algorithms.py	521	# ## References	COMMENT
LOW	notebooks/nlp_apps.py	1	# ---	COMMENT
LOW	notebooks/nlp_apps.py	21	# In this notebook we will take a look at some indicative applications of natural language processing. We will cover con	COMMENT
LOW	notebooks/nlp_apps.py	101		COMMENT
LOW	notebooks/nlp_apps.py	161		COMMENT
LOW	notebooks/nlp_apps.py	181	# Let's now take a look at a harder problem, classifying the authors of the [Federalist Papers](https://en.wikipedia.org	COMMENT
LOW	notebooks/nlp_apps.py	201	# %% [markdown]	COMMENT
LOW	notebooks/nlp_apps.py	261	madison = ''.join(madison)	COMMENT
LOW	notebooks/nlp_apps.py	381	print('Paper No. {}: {}'.format(d, ' '.join(results)))	COMMENT
LOW	notebooks/nlp_apps.py	401	# Some of the broad applications of Text Classification are:-	COMMENT
LOW	notebooks/nlp_apps.py	481	words_1 += sent	COMMENT
LOW	notebooks/planning_total_order_planner.py	1	# ---	COMMENT
LOW	notebooks/planning_total_order_planner.py	21	# In mathematical terminology, total order, linear order or simple order refers to a set X which is said t	COMMENT
LOW	notebooks/planning_total_order_planner.py	41		COMMENT
LOW	notebooks/planning_partial_order_planner.py	1	# ---	COMMENT
LOW	notebooks/planning_partial_order_planner.py	21	# The way a partial-order plan works enables it to take advantage of _problem decomposition_ and work on each subproblem	COMMENT
LOW	notebooks/planning_partial_order_planner.py	41	# We will first describe the data-structures and helper methods used, followed by the algorithm used to find a partial-o	COMMENT
LOW	notebooks/planning_partial_order_planner.py	61	# `causal_links` stores this information as tuples `(Action(A), precondition(p), Action(B))` which is interpreted as giv	COMMENT
LOW	notebooks/planning_partial_order_planner.py	81	# <br>	COMMENT
LOW	notebooks/planning_partial_order_planner.py	101	# %% [markdown]	COMMENT
LOW	notebooks/planning_partial_order_planner.py	121	# <br>	COMMENT
LOW	notebooks/planning_partial_order_planner.py	161	# <br>	COMMENT
LOW	notebooks/planning_partial_order_planner.py	181	# %%	COMMENT
LOW	notebooks/index.py	1	# ---	COMMENT
LOW	notebooks/index.py	21	# Welcome to the AIMA Python Code Repository. You should be seeing this index notebook if you clicked on the **Launch Bi	COMMENT
LOW	notebooks/index.py	41	# 8. [Probability](./probability.ipynb)	COMMENT
LOW	notebooks/knowledge_version_space.py	1	# ---	COMMENT
LOW	notebooks/knowledge_version_space.py	21	# The [knowledge](https://github.com/aimacode/aima-python/blob/master/knowledge.py) module covers **Chapter 19: Knowledg	COMMENT
561 more matches not shown…

Unused Imports140 hits · 128 pts

Severity	File	Line	Context
LOW	tests/test_utils.py	2	CODE
LOW	tests/test_perception.py	5	CODE
LOW	tests/test_csp.py	3	CODE
LOW	tests/test_deep_learning.py	4	CODE
LOW	tests/test_search.py	2	CODE
LOW	tests/test_knowledge.py	3	CODE
LOW	tests/test_graphplan.py	5	CODE
LOW	tests/test_learning.py	3	CODE
LOW	tests/test_probabilistic_learning.py	6	CODE
LOW	tests/test_text.py	6	CODE
LOW	tests/test_reinforcement_learning.py	3	CODE
LOW	tests/test_nlp.py	10	CODE
LOW	tests/test_mdp.py	3	CODE
LOW	tests/test_logic.py	3	CODE
LOW	tests/test_games.py	3	CODE
LOW	tests/test_planning.py	5	CODE
LOW	tests/test_probability.py	3	CODE
LOW	gui/romania_problem.py	4	CODE
LOW	gui/tic-tac-toe.py	2	CODE
LOW	gui/vacuum_agent.py	2	CODE
LOW	gui/vacuum_agent.py	4	CODE
LOW	gui/grid_mdp.py	16	CODE
LOW	gui/genetic_algorithm_example.py	13	CODE
LOW	gui/tsp.py	1	CODE
LOW	gui/tsp.py	5	CODE
LOW	gui/eight_puzzle.py	5	CODE
LOW	gui/xy_vacuum_environment.py	2	CODE
LOW	gui/xy_vacuum_environment.py	4	CODE
LOW	aima/making_simple_decisions.py	9	CODE
LOW	aima/making_simple_decisions.py	9	CODE
LOW	aima/utils.py	3	CODE
LOW	aima/search.py	12	CODE
LOW	aima/probability.py	10	CODE
LOW	aima/learning.py	9	CODE
LOW	notebooks/knowledge_current_best.py	26	CODE
LOW	notebooks/improving_sat_algorithms.py	31	CODE
LOW	notebooks/improving_sat_algorithms.py	238	CODE
LOW	notebooks/nlp_apps.py	44	CODE
LOW	notebooks/nlp_apps.py	123	CODE
LOW	notebooks/nlp_apps.py	191	CODE
LOW	notebooks/nlp_apps.py	281	CODE
LOW	notebooks/planning_total_order_planner.py	39	CODE
LOW	notebooks/planning_partial_order_planner.py	34	CODE
LOW	notebooks/knowledge_version_space.py	26	CODE
LOW	notebooks/arc_consistency_heuristics.py	34	CODE
LOW	notebooks/mdp_apps.py	26	CODE
LOW	notebooks/mdp_apps.py	27	CODE
LOW	notebooks/mdp_apps.py	27	CODE
LOW	notebooks/planning_hierarchical_search.py	48	CODE
LOW	notebooks/learning_apps.py	24	CODE
LOW	notebooks/learning_apps.py	25	CODE
LOW	notebooks/learning_apps.py	26	CODE
LOW	notebooks/game_theory.py	24	CODE
LOW	notebooks/planning_graph_plan.py	64	CODE
LOW	notebooks/csp.py	24	CODE
LOW	notebooks/planning_angelic_search.py	29	CODE
LOW	notebooks/sarsa.py	24	CODE
LOW	notebooks/games.py	36	CODE
LOW	notebooks/reinforcement_learning.py	24	CODE
LOW	notebooks/logic.py	27	CODE
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Hyper-Verbose Identifiers117 hits · 126 pts

Severity	File	Line	Snippet	Context
LOW	tests/test_utils.py	284	def test_priority_queue_with_objects():	CODE
LOW	tests/test_utils.py	302	def test_priority_queue_ties_with_non_comparable_items():	CODE
LOW⚡	tests/test_perception.py	18	def test_sum_squared_difference():	CODE
LOW⚡	tests/test_perception.py	28	def test_gen_gray_scale_picture():	CODE
LOW⚡	tests/test_perception.py	36	def test_generate_edge_weight():	CODE
LOW	tests/test_game_theory.py	35	def test_pure_nash_equilibria():	CODE
LOW	tests/test_game_theory.py	125	def test_zero_sum_game_with_saddle_point():	CODE
LOW	tests/test_game_theory.py	161	def test_alternating_offers_bargaining():	CODE
LOW	tests/test_csp.py	38	def test_csp_count_lost_values():	CODE
LOW	tests/test_csp.py	131	def test_csp_infer_assignment():	CODE
LOW	tests/test_csp.py	279	def test_first_unassigned_variable():	CODE
LOW	tests/test_csp.py	326	def test_unordered_domain_values():	CODE
LOW	tests/test_csp.py	562	def test_different_values_constraint():	CODE
LOW	tests/test_csp.py	704	def test_constraint_check_count():	CODE
LOW⚡	tests/test_search.py	19	def test_breadth_first_tree_search():	CODE
LOW⚡	tests/test_search.py	25	def test_breadth_first_graph_search():	CODE
LOW⚡	tests/test_search.py	29	def test_best_first_graph_search():	CODE
LOW⚡	tests/test_search.py	51	def test_depth_first_tree_search():	CODE
LOW⚡	tests/test_search.py	55	def test_depth_first_graph_search():	CODE
LOW⚡	tests/test_search.py	60	def test_iterative_deepening_search():	CODE
LOW⚡	tests/test_search.py	65	def test_depth_limited_search():	CODE
LOW⚡	tests/test_search.py	73	def test_bidirectional_search():	CODE
LOW	tests/test_search.py	87	def test_tree_search_variants():	CODE
LOW	tests/test_search.py	100	def test_iterative_deepening_astar_search():	CODE
LOW	tests/test_search.py	206	def test_recursive_best_first_search():	CODE
LOW	tests/test_search.py	385	def test_simple_problem_solving_agent():	CODE
LOW	tests/test_search.py	500	def test_grid_search_visualization():	CODE
LOW	tests/test_search.py	515	def test_local_search_variants():	CODE
LOW	tests/test_knowledge.py	55	def test_current_best_learning():	CODE
LOW	tests/test_knowledge.py	78	def test_version_space_learning():	CODE
LOW	tests/test_knowledge.py	94	def test_minimal_consistent_det():	CODE
LOW	tests/test_graphplan.py	35	def test_generalized_blocksworld_manual():	CODE
LOW	tests/test_graphplan.py	85	def test_have_cake_and_eat_cake_too():	CODE
LOW	tests/test_graphplan.py	111	def test_logistics_plan_valid(goal_state):	CODE
LOW	tests/test_graphplan.py	117	def test_rush_hour_manual_alt_sequence():	CODE
LOW	tests/test_graphplan.py	178	def test_impossible_cake_exits_via_leveloff():	CODE
LOW	tests/test_learning.py	51	def test_decision_tree_learner():	CODE
LOW	tests/test_learning.py	99	def test_neural_network_learner():	CODE
LOW	tests/test_learning.py	207	def test_decision_list_learner():	CODE
LOW	tests/test_text.py	143	def test_viterbi_segmentation():	CODE
LOW	tests/test_text.py	194	def test_counting_probability_distribution():	CODE
LOW	tests/test_mdp.py	154	def test_pomdp_value_iteration():	CODE
LOW	tests/test_logic.py	102	def test_parse_definite_clause():	CODE
LOW	tests/test_logic.py	161	def test_dpll_branching_heuristics():	CODE
LOW	tests/test_logic.py	171	def test_cdcl_restart_strategies():	CODE
LOW	tests/test_logic.py	270	def test_eliminate_implications():	CODE
LOW	tests/test_logic.py	293	def test_distribute_and_over_or():	CODE
LOW	tests/test_logic.py	331	def test_standardize_variables():	CODE
LOW	tests/test_logic.py	408	def test_hybrid_wumpus_agent_make_percept_sentence():	CODE
LOW⚡	tests/test_logic.py	419	def test_hybrid_wumpus_agent_plan_shot():	CODE
LOW⚡	tests/test_logic.py	428	def test_hybrid_wumpus_agent_plan_route_no_path():	CODE
LOW⚡	tests/test_logic.py	434	def test_hybrid_wumpus_agent_first_action():	CODE
LOW	tests/test_games.py	59	def test_monte_carlo_tree_search():	CODE
LOW	tests/test_planning.py	145	def test_have_cake_and_eat_cake_too():	CODE
LOW	tests/test_planning.py	467	def test_find_open_precondition():	CODE
LOW	tests/test_planning.py	505	def test_partial_order_planner():	CODE
LOW	tests/test_planning.py	518	def test_partial_order_planner_solves_standard_problems():	CODE
LOW	tests/test_planning.py	657	def test_hierarchical_search_max_depth():	CODE
LOW	tests/test_planning.py	728	def test_optimistic_reachable_set():	CODE
LOW	tests/test_planning.py	743	def test_pessimistic_reachable_set():	CODE
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Deep Nesting99 hits · 97 pts

Severity	File	Line	Context
LOW	tests/test_search.py	385	CODE
LOW	tests/test_search.py	398	CODE
LOW	docs/conf.py	68	CODE
LOW	gui/romania_problem.py	446	CODE
LOW	gui/romania_problem.py	503	CODE
LOW	gui/romania_problem.py	559	CODE
LOW	gui/vacuum_agent.py	37	CODE
LOW	gui/grid_mdp.py	132	CODE
LOW	gui/xy_vacuum_environment.py	63	CODE
LOW	gui/xy_vacuum_environment.py	74	CODE
LOW	gui/xy_vacuum_environment.py	105	CODE
LOW	gui/xy_vacuum_environment.py	128	CODE
LOW	aima/deep_learning.py	350	CODE
LOW	aima/deep_learning.py	382	CODE
LOW	aima/game_theory.py	41	CODE
LOW	aima/game_theory.py	53	CODE
LOW	aima/csp.py	200	CODE
LOW	aima/csp.py	241	CODE
LOW	aima/csp.py	295	CODE
LOW	aima/csp.py	333	CODE
LOW	aima/csp.py	428	CODE
LOW	aima/csp.py	449	CODE
LOW	aima/csp.py	863	CODE
LOW	aima/csp.py	453	CODE
LOW	aima/csp.py	745	CODE
LOW	aima/csp.py	1092	CODE
LOW	aima/csp.py	1290	CODE
LOW	aima/csp.py	1321	CODE
LOW	aima/csp.py	1394	CODE
LOW	aima/csp.py	1447	CODE
LOW	aima/logic.py	285	CODE
LOW	aima/logic.py	362	CODE
LOW	aima/logic.py	561	CODE
LOW	aima/logic.py	918	CODE
LOW	aima/logic.py	1816	CODE
LOW	aima/logic.py	1868	CODE
LOW	aima/logic.py	1884	CODE
LOW	aima/logic.py	1920	CODE
LOW	aima/logic.py	2069	CODE
LOW	aima/logic.py	2128	CODE
LOW	aima/logic.py	2185	CODE
LOW	aima/logic.py	2218	CODE
LOW	aima/logic.py	1372	CODE
LOW	aima/logic.py	1601	CODE
LOW	aima/agents.py	255	CODE
LOW	aima/agents.py	266	CODE
LOW	aima/agents.py	410	CODE
LOW	aima/agents.py	448	CODE
LOW	aima/agents.py	506	CODE
LOW	aima/agents.py	820	CODE
LOW	aima/agents.py	1006	CODE
LOW	aima/nlp.py	366	CODE
LOW	aima/nlp.py	492	CODE
LOW	aima/nlp.py	433	CODE
LOW	aima/nlp.py	501	CODE
LOW	aima/mdp.py	343	CODE
LOW	aima/planning.py	50	CODE
LOW	aima/planning.py	90	CODE
LOW	aima/planning.py	1100	CODE
LOW	aima/planning.py	1135	CODE
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AI Slop Vocabulary30 hits · 78 pts

Severity	File	Line	Snippet	Context
MEDIUM	notebooks/improving_sat_algorithms.py	38	# Essentially, `DPLL` is a backtracking depth-first search through partial truth assignments which uses a *splitting rul	COMMENT
MEDIUM	notebooks/improving_sat_algorithms.py	169	# Implementation issues for SAT solvers include the design of suitable data structures for storing clauses. The implemen	COMMENT
MEDIUM	notebooks/nlp_apps.py	219	# NOTE: Since we are only removing a single word from each paper, this step can be skipped. We add it here to show that	COMMENT
MEDIUM	notebooks/planning_total_order_planner.py	34	# In the module, the `Linearize` class solves problems using this paradigm.	COMMENT
LOW	notebooks/mdp_apps.py	198	# This is not a conventional term, we just use it to minimize confusion between the two.	COMMENT
LOW	notebooks/csp.py	100	# The _ ___init___ _ method takes only one parameter n i.e. the size of the problem. To create an instance, we just	COMMENT
MEDIUM	notebooks/games.py	254	# It is the turn of MAX to move, and he is at state A. He can move to B, C or D, using moves a1, a2 and a3 respectively.	COMMENT
MEDIUM	notebooks/games.py	340	# Below we see the best moves for MIN starting from B, C and D respectively. Note that the algorithm in these cases work	COMMENT
MEDIUM	notebooks/logic.py	873	# In this section we look at a forward chaining and a backward chaining algorithm for `FolKB`. Both aforementioned algor	COMMENT
MEDIUM	notebooks/nlp.py	326	# Hyperlink-Induced Topic Search (or HITS for short) is an algorithm for information retrieval and page ranking. You	COMMENT
MEDIUM	notebooks/nlp.py	326	# Hyperlink-Induced Topic Search (or HITS for short) is an algorithm for information retrieval and page ranking. You	COMMENT
LOW	notebooks/nlp.py	549	# And then we simply call the `parses` function:	COMMENT
MEDIUM	notebooks/search.py	67	# * Informed search algorithms: These type of algorithms leverage any information (heuristics, path cost) on the pro	COMMENT
MEDIUM	notebooks/search.py	1849	# Next we need to come up with a fitness function that appropriately scores individuals. Again, we will look at the prob	COMMENT
MEDIUM	notebooks/search.py	1931	# A gene's value in an individual `q` denotes the queen's column, and the position of the gene denotes its row. We can c	COMMENT
LOW	notebooks/search.py	231	# We can simply call the function with node_colors dictionary object to display it.	COMMENT
LOW	notebooks/search.py	1687	# There has to be some way of determining which phrases in our population have a better chance of eventually evolving in	COMMENT
LOW	notebooks/search.py	1723	# Great! Now, we need to define the most important metric for the genetic algorithm, i.e the fitness function. This will	COMMENT
MEDIUM	notebooks/learning.py	215	# Currently the `iris` dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a bi	COMMENT
MEDIUM	notebooks/learning.py	370	# The Plurality Learner is a simple algorithm, used mainly as a baseline comparison for other algorithms. It finds the m	COMMENT
MEDIUM	notebooks/learning.py	535	# As the name of the algorithm and image above suggest, this algorithm creates the forest with a number of trees. The mo	COMMENT
MEDIUM	notebooks/learning.py	594	# We have a dataset with a set of classes (C) and we want to classify an item with a set of features (F). Essent	COMMENT
MEDIUM	notebooks/neural_nets.py	39	# After the input values are fed-forward into the network, the resulting output can be used for classification. The prob	COMMENT
LOW	notebooks/classical_planning_approaches.py	155	# A propositional planning procedure implements the basic idea just given but, because the agent does not know how many	COMMENT
MEDIUM	notebooks/chapter19/RNN.py	79	# Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representatio	COMMENT
MEDIUM	notebooks/chapter18/Decision Tree.py	153	# As the name of the algorithm and image above suggest, this algorithm creates the forest with a number of trees. The mo	COMMENT
MEDIUM	notebooks/chapter18/Datasets.py	188	# Currently the iris dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a bina	COMMENT
MEDIUM	notebooks/chapter22/nlp_apps.py	221	# NOTE: Since we are only removing a single word from each paper, this step can be skipped. We add it here to show that	COMMENT
LOW	notebooks/chapter22/Parsing.py	85	# And then we simply call the `parses` function:	COMMENT
MEDIUM	notebooks/chapter24/Image Edge Detection.py	99	# When considering the situation when there is strong noise in an image, the ups and downs of the noise will induce stro	COMMENT

Cross-File Repetition12 hits · 60 pts

Severity	File	Snippet	Context
HIGH	gui/romania_problem.py	search the nodes with the lowest f scores first. you specify the function f(node) that you want to minimize; for example	STRING
HIGH	aima/search.py	search the nodes with the lowest f scores first. you specify the function f(node) that you want to minimize; for example	STRING
HIGH	notebooks/search.py	search the nodes with the lowest f scores first. you specify the function f(node) that you want to minimize; for example	STRING
HIGH	gui/romania_problem.py	a* search is best-first graph search with f(n) = g(n)+h(n). you need to specify the h function when you call astar_searc	STRING
HIGH	aima/search.py	a* search is best-first graph search with f(n) = g(n)+h(n). you need to specify the h function when you call astar_searc	STRING
HIGH	notebooks/search.py	a* search is best-first graph search with f(n) = g(n)+h(n). you need to specify the h function when you call astar_searc	STRING
HIGH	aima/csp.py	wa: or id; or: id nv ca; ca: nv az; nv: id ut az; id: mt wy ut; ut: wy co az; mt: nd sd wy; wy: sd ne co; co: ne ka ok n	STRING
HIGH	aima/logic.py	wa: or id; or: id nv ca; ca: nv az; nv: id ut az; id: mt wy ut; ut: wy co az; mt: nd sd wy; wy: sd ne co; co: ne ka ok n	STRING
HIGH	notebooks/improving_sat_algorithms.py	wa: or id; or: id nv ca; ca: nv az; nv: id ut az; id: mt wy ut; ut: wy co az; mt: nd sd wy; wy: sd ne co; co: ne ka ok n	STRING
HIGH	aima/csp.py	al: lo fc; aq: mp li pc; au: li ce bo ra lr mp; bo: ce if ca fc ra au; br: nb pl; ca: if pi lo fc bo; ce: pl nb nh if bo	STRING
HIGH	aima/logic.py	al: lo fc; aq: mp li pc; au: li ce bo ra lr mp; bo: ce if ca fc ra au; br: nb pl; ca: if pi lo fc bo; ce: pl nb nh if bo	STRING
HIGH	notebooks/improving_sat_algorithms.py	al: lo fc; aq: mp li pc; au: li ce bo ra lr mp; bo: ce if ca fc ra au; br: nb pl; ca: if pi lo fc bo; ce: pl nb nh if bo	STRING

Self-Referential Comments14 hits · 40 pts

Severity	File	Line	Snippet	Context
MEDIUM	tests/test_games.py	5	# Creating the game instances	COMMENT
MEDIUM	aima/agents.py	473	"""This class is for environments on a 2D plane, with locations	STRING
MEDIUM	notebooks/planning_graph_plan.py	99	# This method is invoked when the goal conditions haven't been met in the current level or the actions that lead to it a	COMMENT
MEDIUM	notebooks/mdp.py	647	# This function is represented as `b' = FORWARD(b, a, e)`	COMMENT
MEDIUM	notebooks/mdp.py	733	# Defining a POMDP is useless unless we can find a way to solve it. As POMDPs can have infinitely many belief states, we	COMMENT
MEDIUM	notebooks/planning.py	109	# This class represents an action given the expression, the preconditions and its effects.	COMMENT
MEDIUM	notebooks/search.py	1412	# Define a problem with these parameters.	COMMENT
MEDIUM	notebooks/probability.py	1125	# Define the sensor model as a function `P_sensor`.	COMMENT
MEDIUM	notebooks/vacuum_world.py	89	# Create the random agent	COMMENT
MEDIUM	notebooks/vacuum_world.py	136	# Create a table-driven agent	COMMENT
MEDIUM	notebooks/vacuum_world.py	196	# Create a simple reflex agent the two-state environment	COMMENT
MEDIUM	notebooks/vacuum_world.py	241	# Create a model-based reflex agent	COMMENT
MEDIUM	notebooks/chapter16-17/Partially Observable MDP.py	127	# This function is represented as `b' = FORWARD(b, a, e)`	COMMENT
MEDIUM	notebooks/chapter16-17/Partially Observable MDP.py	216	# Defining a POMDP is useless unless we can find a way to solve it. As POMDPs can have infinitely many belief states, we	COMMENT

Slop Phrases11 hits · 32 pts

Severity	File	Line	Snippet	Context
LOW	notebooks/knowledge_current_best.py	76	# When specializing or generalizing, we should make sure to not create inconsistencies with previous examples. To avoid	COMMENT
MEDIUM	notebooks/knowledge_current_best.py	87	# As mentioned earlier, examples are dictionaries (with keys being the attribute names) and hypotheses are lists of dict	COMMENT
MEDIUM	notebooks/mdp_apps.py	775	# Perhaps you can try this as an exercise.	COMMENT
MEDIUM	notebooks/games.py	105	# The class `TicTacToe` has been inherited from the class `Game`. As mentioned earlier, you really want to do this. Catc	COMMENT
MEDIUM	notebooks/logic.py	130	# If the `\|'==>'\|` notation looks ugly to you, you can use the function `expr` instead:	COMMENT
MEDIUM	notebooks/intro.py	57	# From there, the notebook alternates explanations with examples of use. You can run the examples as they are, and you c	COMMENT
MEDIUM	notebooks/intro.py	59	# If you want to see the source code of a function, you can open a browser or editor and see it in another window, or fr	COMMENT
MEDIUM	notebooks/chapter19/Optimizer and Backpropagation.py	58	# To view the pseudocode and the implementation, you can use the following codes:	COMMENT
MEDIUM	notebooks/chapter18/Linear and Nonparametric Models.py	131	# Which is the same as expected. By altering k, you can change the number of neighbors considering in the lookup procedu	COMMENT
MEDIUM	notebooks/chapter18/Decision Tree.py	96	# Then you can try a new example on the trained learner:	COMMENT
MEDIUM	notebooks/chapter24/Objects in Images.py	82	# Within 5 epochs of training, the model accuracy on the training set improves from 35% to 42% while validation accuracy	COMMENT

Excessive Try-Catch Wrapping10 hits · 23 pts

Severity	File	Line	Snippet	Context
LOW	lite/verify_browser.py	126	except Exception:	CODE
MEDIUM⚡	notebooks/learning.py	943	print("Error ratio for k=1:", err_ratio(kNN_1, iris))	CODE
MEDIUM⚡	notebooks/learning.py	944	print("Error ratio for k=3:", err_ratio(kNN_3, iris))	CODE
MEDIUM⚡	notebooks/learning.py	945	print("Error ratio for k=5:", err_ratio(kNN_5, iris))	CODE
MEDIUM⚡	notebooks/learning.py	946	print("Error ratio for k=7:", err_ratio(kNN_7, iris))	CODE
MEDIUM	notebooks/learning.py	924	print("Error ratio for Discrete:", err_ratio(nBD, iris))	CODE
MEDIUM	notebooks/learning.py	927	print("Error ratio for Continuous:", err_ratio(nBC, iris))	CODE
MEDIUM	notebooks/learning.py	963	print("Error ratio for Perceptron:", err_ratio(perceptron, iris2))	CODE
MEDIUM	notebooks/learning.py	1025	print("Error ratio for adaboost: ", err_ratio(adaboost, iris2))	CODE
MEDIUM	notebooks/chapter18/Ensemble Learning.py	107	print("Error ratio for adaboost: ", err_ratio(adaboost, iris2))	CODE

Cross-Language Confusion6 hits · 22 pts

Severity	File	Line	Snippet	Context
HIGH	lite/verify_browser.py	18	cd lite && ./build.sh && python verify_browser.py	STRING
HIGH	lite/verify_browser.py	48	pyodide.setStdout({{batched: (s) => window.OUT.push(s)}});	CODE
HIGH	lite/verify_browser.py	49	pyodide.setStderr({{batched: (s) => window.OUT.push(s)}});	CODE
HIGH	lite/verify_browser.py	74	}} catch (e) {{ window.bootErr = e.toString(); }}	CODE
HIGH	lite/verify_browser.py	95	return {ok: false, out: window.OUT.join('') + '\\n' + e.toString()};	STRING
HIGH	tests/test_nlp.py	269	# retest function with values. Should now return false	STRING

Redundant / Tautological Comments5 hits · 8 pts

Severity	File	Line	Snippet	Context
LOW	tests/test_reinforcement_learning.py	26	# Check if results are good enough.	COMMENT
LOW	tests/test_reinforcement_learning.py	39	# Check if results are good enough.	COMMENT
LOW	tests/test_reinforcement_learning.py	52	# Check if results are good enough.	COMMENT
LOW	tests/test_reinforcement_learning.py	65	# Check if results are good enough.	COMMENT
LOW	notebooks/agents.py	433	#Check if agent is about to bump into a wall	COMMENT

AI Response Leakage1 hit · 8 pts

Severity	File	Line	Snippet	Context
HIGH	notebooks/probability.py	736	# Before we start, it will be helpful to understand the structure of a temporal model. We will use the example of the bo	COMMENT

Decorative Section Separators2 hits · 6 pts

Severity	File	Line	Snippet	Context
MEDIUM	tests/test_game_theory.py	106	# ---------------------------------------------------------------------------	COMMENT
MEDIUM	tests/test_game_theory.py	108	# ---------------------------------------------------------------------------	COMMENT

AI Structural Patterns4 hits · 4 pts

Severity	File	Line	Context
LOW	aima/planning.py	1653	CODE
LOW	aima/probability.py	584	CODE
LOW	aima/learning.py	39	CODE
LOW	aima/knowledge.py	355	CODE

Modern AI Meta-Vocabulary1 hit · 3 pts

Severity	File	Line	Snippet	Context
MEDIUM	notebooks/probability.py	1193	# While constructing multi-agent systems, one major element in the design is the mechanism the agents use for making dec	COMMENT

TODO Padding1 hit · 2 pts

Severity	File	Line	Snippet	Context
LOW	tests/test_probability.py	456	# TODO: Add tests for random motion/inaccurate sensors	COMMENT

Overly Generic Function Names1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	gui/grid_mdp.py	573	def process_data(self, terminals, _height, _width, gridmdp):	CODE

Modern Structural Boilerplate1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	aima/making_simple_decisions.py	11	__all__ = ['DecisionNetwork', 'InformationGatheringAgent']	CODE

Analysis Overview

What These Metrics Mean

Score History

Severity Breakdown

Directory Score Breakdown

Pattern Findings