defmodule Dspy.ExperimentalFramework do
  @moduledoc """
  Experimental framework for dynamic novel system generation and learning.

  This module orchestrates comprehensive scientific experimentation cycles with:
  1. Novel system generation
  2. System execution and evaluation  
  3. Training data collection
  4. Pattern learning and optimization
  5. Continuous improvement of generation strategies

  ## Scientific Experiment Design

  The framework supports rigorous experimental methodology:

  - **Hypothesis-driven exploration**: Define testable hypotheses about reasoning approaches
  - **Controlled experimentation**: Systematic parameter variation with control groups
  - **Statistical significance**: Multiple runs with confidence intervals and p-value analysis
  - **Reproducibility**: Complete experiment state serialization and deterministic replay
  - **Meta-analysis**: Cross-experiment pattern detection and learning transfer

  ## Experiment Journal Integration

  Each experiment maintains detailed scientific records:

  - **Research questions**: Clear problem formulation and success criteria
  - **Methodology**: Detailed experimental design and parameter choices
  - **Observations**: Real-time data collection and intermediate results
  - **Analysis**: Statistical analysis and interpretation of results
  - **Conclusions**: Evidence-based findings and future research directions

  ## Example Usage

      # Define research hypothesis
      hypothesis = %{
        question: "Does chain-of-thought improve mathematical reasoning accuracy?",
        method: "Compare CoT vs direct prediction on math problems",
        success_metric: "accuracy > 85% with p < 0.05"
      }

      # Create experiment with journal logging
      framework = Dspy.ExperimentalFramework.new(MathSignature,
        experiment_settings: %{
          hypothesis: hypothesis,
          control_groups: ["direct", "cot", "tree_of_thoughts"],
          sample_size: 100,
          confidence_level: 0.95,
          journal_enabled: true
        }
      )

      # Run systematic experiment
      {:ok, results} = Dspy.Module.forward(framework, inputs)

      # Access scientific analysis
      journal = results.attrs.experiment_journal
      IO.puts(journal.hypothesis_validation.p_value)
      IO.puts(journal.conclusions.effect_size)

  ## Advanced Capabilities

  - **Adaptive experimental design**: Dynamic parameter adjustment based on interim results
  - **Multi-objective optimization**: Balance accuracy, efficiency, and novelty
  - **Cross-validation**: Robust evaluation with stratified sampling
  - **Ensemble methods**: Combine multiple experimental approaches
  - **Transfer learning**: Apply insights across different problem domains
  """

  use Dspy.Module

  defstruct [
    :signature,
    :examples,
    :max_retries,
    :experiment_settings,
    :learning_rate,
    :exploration_probability,
    :evaluation_metrics,
    :meta_learning_enabled,
    :continuous_mode,
    :performance_history,
    generator: nil,
    storage: nil,
    learner: nil
  ]

  @type experiment_settings :: %{
          batch_size: pos_integer(),
          max_iterations: pos_integer(),
          success_threshold: float(),
          novelty_requirement: float(),
          time_budget_ms: pos_integer(),
          parallel_execution: boolean()
        }

  @type evaluation_metrics :: %{
          success_rate: float(),
          average_novelty: float(),
          execution_time: float(),
          resource_efficiency: float(),
          pattern_diversity: float()
        }

  @type t :: %__MODULE__{
          signature: Dspy.Signature.t(),
          examples: [Dspy.Example.t()],
          max_retries: non_neg_integer(),
          experiment_settings: experiment_settings(),
          learning_rate: float(),
          exploration_probability: float(),
          evaluation_metrics: evaluation_metrics(),
          meta_learning_enabled: boolean(),
          continuous_mode: boolean(),
          performance_history: [map()],
          generator: Dspy.NovelSystemGenerator.t(),
          storage: atom(),
          learner: atom()
        }

  def new(signature, opts \\ []) do
    base_signature = get_signature(signature)

    %__MODULE__{
      signature: base_signature,
      examples: Keyword.get(opts, :examples, []),
      max_retries: Keyword.get(opts, :max_retries, 3),
      experiment_settings: Keyword.get(opts, :experiment_settings, default_experiment_settings()),
      learning_rate: Keyword.get(opts, :learning_rate, 0.1),
      exploration_probability: Keyword.get(opts, :exploration_probability, 0.3),
      evaluation_metrics: Keyword.get(opts, :evaluation_metrics, %{}),
      meta_learning_enabled: Keyword.get(opts, :meta_learning_enabled, true),
      continuous_mode: Keyword.get(opts, :continuous_mode, false),
      performance_history: [],
      generator: nil,
      storage: Keyword.get(opts, :storage, Dspy.TrainingDataStorage),
      learner: Keyword.get(opts, :learner, Dspy.MetaLearner)
    }
  end

  @impl true
  def forward(framework, inputs) do
    with :ok <- Dspy.Signature.validate_inputs(framework.signature, inputs),
         {:ok, initialized_framework} <- initialize_components(framework),
         {:ok, experiment_results} <- run_experimental_cycle(initialized_framework, inputs),
         {:ok, learned_insights} <-
           extract_learning_insights(initialized_framework, experiment_results),
         {:ok, optimized_framework} <-
           update_framework_with_learning(initialized_framework, learned_insights) do
      # Return the best result from the experimental cycle
      best_result = select_best_experimental_result(experiment_results)

      enhanced_result =
        best_result
        |> Map.put(:experimental_insights, learned_insights)
        |> Map.put(
          :framework_improvements,
          summarize_improvements(framework, optimized_framework)
        )
        |> Map.put(:total_experiments_run, length(experiment_results))

      prediction = Dspy.Prediction.new(enhanced_result)
      {:ok, prediction}
    else
      {:error, reason} -> {:error, reason}
    end
  end

  defp get_signature(signature) when is_atom(signature) do
    signature.signature()
  end

  defp get_signature(signature), do: signature

  defp initialize_components(framework) do
    # Initialize the novel system generator
    generator =
      Dspy.NovelSystemGenerator.new(
        framework.signature,
        examples: framework.examples,
        max_retries: framework.max_retries,
        novelty_threshold: framework.experiment_settings.novelty_requirement,
        training_data_store: framework.storage
      )

    # Start training data storage if not already running
    case Process.whereis(framework.storage) do
      nil ->
        {:ok, _pid} = framework.storage.start_link()

      _pid ->
        :ok
    end

    initialized_framework = %{framework | generator: generator}
    {:ok, initialized_framework}
  end

  defp run_experimental_cycle(framework, inputs) do
    if framework.continuous_mode do
      run_continuous_experiments(framework, inputs)
    else
      run_batch_experiments(framework, inputs)
    end
  end

  defp run_batch_experiments(framework, inputs) do
    batch_size = framework.experiment_settings.batch_size
    max_iterations = framework.experiment_settings.max_iterations

    results =
      1..max_iterations
      |> Enum.take_while(fn iteration ->
        should_continue_experiments?(framework, iteration)
      end)
      |> Enum.flat_map(fn iteration ->
        run_experiment_batch(framework, inputs, batch_size, iteration)
      end)

    {:ok, results}
  end

  defp run_continuous_experiments(framework, inputs) do
    # In continuous mode, run experiments indefinitely with adaptive parameters
    stream = Stream.iterate(1, &(&1 + 1))

    results =
      stream
      |> Stream.take_while(fn iteration ->
        should_continue_continuous_experiments?(framework, iteration)
      end)
      |> Stream.flat_map(fn iteration ->
        # Adapt experiment parameters based on learning
        adapted_framework = adapt_experiment_parameters(framework, iteration)
        run_experiment_batch(adapted_framework, inputs, 1, iteration)
      end)
      |> Enum.take(framework.experiment_settings.max_iterations)

    {:ok, results}
  end

  defp run_experiment_batch(framework, inputs, batch_size, iteration) do
    # Run multiple experiments in parallel
    experiment_tasks =
      1..batch_size
      |> Enum.map(fn batch_index ->
        Task.async(fn ->
          run_single_experiment(framework, inputs, iteration, batch_index)
        end)
      end)

    results = Task.await_many(experiment_tasks, framework.experiment_settings.time_budget_ms)

    # Filter successful results
    results
    |> Enum.filter(fn
      {:ok, _result} -> true
      _ -> false
    end)
    |> Enum.map(fn {:ok, result} -> result end)
  end

  defp run_single_experiment(framework, inputs, iteration, batch_index) do
    experiment_id = "exp_#{iteration}_#{batch_index}_#{System.unique_integer([:positive])}"

    # Add exploration randomization to inputs
    modified_inputs = add_exploration_variation(inputs, framework.exploration_probability)

    # Run the novel system generator
    case Dspy.Module.forward(framework.generator, modified_inputs) do
      {:ok, prediction} ->
        result = %{
          experiment_id: experiment_id,
          iteration: iteration,
          batch_index: batch_index,
          inputs: modified_inputs,
          prediction: prediction,
          timestamp: DateTime.utc_now(),
          success: evaluate_experiment_success(prediction, framework),
          performance_metrics: calculate_performance_metrics(prediction, framework)
        }

        {:ok, result}

      {:error, reason} ->
        {:error, %{experiment_id: experiment_id, reason: reason}}
    end
  end

  defp should_continue_experiments?(framework, iteration) do
    # Check various stopping conditions
    max_iterations_reached = iteration >= framework.experiment_settings.max_iterations
    success_threshold_met = check_success_threshold_met(framework)
    time_budget_exceeded = check_time_budget_exceeded(framework)

    not (max_iterations_reached or success_threshold_met or time_budget_exceeded)
  end

  defp should_continue_continuous_experiments?(framework, iteration) do
    # In continuous mode, check for convergence or plateau
    recent_performance = get_recent_performance(framework, 10)

    has_plateau = detect_performance_plateau(recent_performance)
    max_iterations_reached = iteration >= framework.experiment_settings.max_iterations

    not (has_plateau or max_iterations_reached)
  end

  defp adapt_experiment_parameters(framework, iteration) do
    # Adapt parameters based on performance history
    if framework.meta_learning_enabled and iteration > 10 do
      recent_results = get_recent_performance(framework, 10)

      # Adjust exploration probability based on recent success
      avg_success = calculate_average_success(recent_results)

      new_exploration =
        if avg_success < 0.5 do
          min(0.8, framework.exploration_probability + 0.1)
        else
          max(0.1, framework.exploration_probability - 0.05)
        end

      # Adjust novelty requirements
      avg_novelty = calculate_average_novelty(recent_results)

      novelty_adjustment =
        if avg_novelty < framework.experiment_settings.novelty_requirement do
          -0.05
        else
          0.02
        end

      updated_settings = %{
        framework.experiment_settings
        | novelty_requirement:
            max(0.1, framework.experiment_settings.novelty_requirement + novelty_adjustment)
      }

      %{
        framework
        | exploration_probability: new_exploration,
          experiment_settings: updated_settings
      }
    else
      framework
    end
  end

  defp add_exploration_variation(inputs, exploration_probability) do
    if :rand.uniform() < exploration_probability do
      # Add some variation to encourage exploration
      exploration_variations = [
        "Consider alternative approaches",
        "Think creatively about this problem",
        "Explore unconventional solutions",
        "Challenge standard assumptions"
      ]

      variation = Enum.random(exploration_variations)

      Map.update(inputs, :additional_instructions, variation, fn existing ->
        "#{existing}. #{variation}"
      end)
    else
      inputs
    end
  end

  defp evaluate_experiment_success(prediction, framework) do
    # Check multiple success criteria
    attrs = prediction.attrs

    confidence_ok =
      Map.get(attrs, :confidence, 0) >= framework.experiment_settings.success_threshold

    novelty_ok =
      Map.get(attrs, :novelty_score, 0) >= framework.experiment_settings.novelty_requirement

    # Additional domain-specific success checks could be added here

    confidence_ok and novelty_ok
  end

  defp calculate_performance_metrics(prediction, _framework) do
    attrs = prediction.attrs

    %{
      confidence: Map.get(attrs, :confidence, 0),
      novelty_score: Map.get(attrs, :novelty_score, 0),
      execution_time: Map.get(attrs, :execution_time, 0),
      resource_usage: Map.get(attrs, :resource_usage, %{}),
      complexity_score: calculate_complexity_score(attrs)
    }
  end

  defp calculate_complexity_score(attrs) do
    # Estimate complexity based on various factors
    base_complexity = 0.5

    reasoning_length = String.length(Map.get(attrs, :reasoning, ""))
    length_factor = min(reasoning_length / 1000.0, 0.3)

    insights_count = length(Map.get(attrs, :novel_insights, []))
    insights_factor = insights_count * 0.1

    min(1.0, base_complexity + length_factor + insights_factor)
  end

  defp extract_learning_insights(framework, experiment_results) do
    if framework.meta_learning_enabled do
      insights = %{
        successful_patterns: analyze_successful_patterns(experiment_results),
        failure_modes: analyze_failure_modes(experiment_results),
        performance_trends: analyze_performance_trends(experiment_results),
        optimal_parameters: identify_optimal_parameters(experiment_results),
        novel_discoveries: identify_novel_discoveries(experiment_results),
        efficiency_insights: analyze_efficiency_patterns(experiment_results)
      }

      {:ok, insights}
    else
      {:ok, %{}}
    end
  end

  defp analyze_successful_patterns(results) do
    successful_results = Enum.filter(results, & &1.success)

    if length(successful_results) > 0 do
      %{
        count: length(successful_results),
        average_confidence: calculate_average_confidence(successful_results),
        average_novelty: calculate_average_novelty(successful_results),
        common_characteristics: extract_common_characteristics(successful_results)
      }
    else
      %{count: 0}
    end
  end

  defp analyze_failure_modes(results) do
    failed_results = Enum.reject(results, & &1.success)

    %{
      count: length(failed_results),
      common_issues: extract_common_failure_issues(failed_results),
      failure_rate_by_iteration: calculate_failure_rate_by_iteration(failed_results)
    }
  end

  defp analyze_performance_trends(results) do
    # Analyze how performance changes over iterations
    performance_by_iteration =
      results
      |> Enum.group_by(& &1.iteration)
      |> Enum.map(fn {iteration, iter_results} ->
        avg_confidence = calculate_average_confidence(iter_results)
        avg_novelty = calculate_average_novelty(iter_results)
        success_rate = Enum.count(iter_results, & &1.success) / length(iter_results)

        {iteration,
         %{
           average_confidence: avg_confidence,
           average_novelty: avg_novelty,
           success_rate: success_rate
         }}
      end)
      |> Map.new()

    %{
      performance_by_iteration: performance_by_iteration,
      overall_trend: detect_overall_trend(performance_by_iteration),
      improvement_rate: calculate_improvement_rate(performance_by_iteration)
    }
  end

  defp identify_optimal_parameters(results) do
    # Find parameter settings that led to best results
    successful_results = Enum.filter(results, & &1.success)

    if length(successful_results) > 0 do
      top_performers =
        successful_results
        |> Enum.sort_by(
          fn result ->
            result.performance_metrics.confidence + result.performance_metrics.novelty_score
          end,
          :desc
        )
        |> Enum.take(min(5, length(successful_results)))

      %{
        top_performing_experiments: Enum.map(top_performers, & &1.experiment_id),
        optimal_confidence_range: calculate_optimal_range(top_performers, :confidence),
        optimal_novelty_range: calculate_optimal_range(top_performers, :novelty_score)
      }
    else
      %{}
    end
  end

  defp identify_novel_discoveries(results) do
    # Identify particularly novel or interesting discoveries
    highly_novel =
      results
      |> Enum.filter(fn result ->
        result.performance_metrics.novelty_score > 0.8
      end)
      |> Enum.sort_by(& &1.performance_metrics.novelty_score, :desc)
      |> Enum.take(3)

    %{
      highly_novel_count: length(highly_novel),
      top_novel_experiments: Enum.map(highly_novel, & &1.experiment_id),
      novel_characteristics: extract_novel_characteristics(highly_novel)
    }
  end

  defp analyze_efficiency_patterns(results) do
    %{
      average_execution_time: calculate_average_execution_time(results),
      resource_efficiency_trend: analyze_resource_efficiency(results),
      time_vs_quality_tradeoff: analyze_time_quality_tradeoff(results)
    }
  end

  defp update_framework_with_learning(framework, insights) do
    if framework.meta_learning_enabled and map_size(insights) > 0 do
      # Update framework parameters based on learning insights
      updated_framework =
        framework
        |> update_exploration_strategy(insights)
        |> update_success_thresholds(insights)
        |> update_experiment_settings(insights)
        |> add_performance_history(insights)

      {:ok, updated_framework}
    else
      {:ok, framework}
    end
  end

  defp update_exploration_strategy(framework, insights) do
    if Map.has_key?(insights, :performance_trends) do
      trend = insights.performance_trends.overall_trend

      new_exploration =
        case trend do
          :improving -> max(0.1, framework.exploration_probability - 0.05)
          :declining -> min(0.8, framework.exploration_probability + 0.1)
          _ -> framework.exploration_probability
        end

      %{framework | exploration_probability: new_exploration}
    else
      framework
    end
  end

  defp update_success_thresholds(framework, insights) do
    if Map.has_key?(insights, :optimal_parameters) and
         Map.has_key?(insights.optimal_parameters, :optimal_confidence_range) do
      {min_conf, max_conf} = insights.optimal_parameters.optimal_confidence_range
      new_threshold = (min_conf + max_conf) / 2

      updated_settings = %{
        framework.experiment_settings
        | success_threshold: new_threshold
      }

      %{framework | experiment_settings: updated_settings}
    else
      framework
    end
  end

  defp update_experiment_settings(framework, insights) do
    # Update various experiment settings based on insights
    settings = framework.experiment_settings

    updated_settings =
      settings
      |> maybe_update_batch_size(insights)
      |> maybe_update_time_budget(insights)
      |> maybe_update_novelty_requirement(insights)

    %{framework | experiment_settings: updated_settings}
  end

  defp add_performance_history(framework, insights) do
    history_entry = %{
      timestamp: DateTime.utc_now(),
      insights: insights,
      parameters: framework.experiment_settings
    }

    updated_history =
      [history_entry | framework.performance_history]
      # Keep last 50 entries
      |> Enum.take(50)

    %{framework | performance_history: updated_history}
  end

  defp select_best_experimental_result(results) do
    if length(results) > 0 do
      results
      |> Enum.filter(& &1.success)
      |> case do
        [] ->
          # No successful results, return best unsuccessful one
          Enum.max_by(results, fn result ->
            result.performance_metrics.confidence + result.performance_metrics.novelty_score
          end)

        successful ->
          # Return best successful result
          Enum.max_by(successful, fn result ->
            result.performance_metrics.confidence + result.performance_metrics.novelty_score
          end)
      end
      |> Map.get(:prediction)
      |> Map.get(:attrs, %{})
    else
      %{error: "No experimental results generated"}
    end
  end

  defp summarize_improvements(original_framework, updated_framework) do
    %{
      exploration_probability_change:
        updated_framework.exploration_probability - original_framework.exploration_probability,
      success_threshold_change:
        updated_framework.experiment_settings.success_threshold -
          original_framework.experiment_settings.success_threshold,
      parameters_learned: map_size(Map.get(updated_framework, :learned_parameters, %{}))
    }
  end

  # Helper functions

  defp default_experiment_settings do
    %{
      batch_size: 3,
      max_iterations: 10,
      success_threshold: 0.7,
      novelty_requirement: 0.6,
      time_budget_ms: 60_000,
      parallel_execution: true
    }
  end

  defp check_success_threshold_met(_framework) do
    # Placeholder - would check recent success rate
    false
  end

  defp check_time_budget_exceeded(_framework) do
    # Placeholder - would check elapsed time
    false
  end

  defp get_recent_performance(_framework, _count) do
    # Placeholder - would return recent performance data
    []
  end

  defp detect_performance_plateau(recent_performance) do
    # Check if performance has plateaued (no significant improvement in recent experiments)
    case recent_performance do
      [] ->
        false

      # Need at least 2 data points
      [_] ->
        false

      performance_list when length(performance_list) >= 5 ->
        # Check if the last 5 performance scores are within a small variance
        scores = Enum.map(performance_list, fn %{score: score} -> score end)
        mean = Enum.sum(scores) / length(scores)

        variance =
          Enum.sum(Enum.map(scores, fn score -> :math.pow(score - mean, 2) end)) / length(scores)

        # Plateau if variance is very low
        variance < 0.01

      # Not enough data to determine plateau
      _ ->
        false
    end
  end

  defp calculate_average_success(results) do
    if length(results) > 0 do
      Enum.count(results, & &1.success) / length(results)
    else
      0.0
    end
  end

  defp calculate_average_novelty(results) do
    if length(results) > 0 do
      results
      |> Enum.map(& &1.performance_metrics.novelty_score)
      |> Enum.sum()
      |> Kernel./(length(results))
    else
      0.0
    end
  end

  defp calculate_average_confidence(results) do
    if length(results) > 0 do
      results
      |> Enum.map(& &1.performance_metrics.confidence)
      |> Enum.sum()
      |> Kernel./(length(results))
    else
      0.0
    end
  end

  defp extract_common_characteristics(_results) do
    # Placeholder - would analyze common patterns in successful results
    ["high_confidence", "novel_approach", "efficient_execution"]
  end

  defp extract_common_failure_issues(_failed_results) do
    # Placeholder - would analyze common failure patterns
    ["low_novelty", "insufficient_reasoning", "resource_constraints"]
  end

  defp calculate_failure_rate_by_iteration(_failed_results) do
    # Placeholder - would calculate how failure rate changes over iterations
    %{}
  end

  defp detect_overall_trend(_performance_by_iteration) do
    # Placeholder - would detect if performance is improving, declining, or stable
    :stable
  end

  defp calculate_improvement_rate(_performance_by_iteration) do
    # Placeholder - would calculate rate of improvement
    0.0
  end

  defp calculate_optimal_range(results, metric_key) do
    values = Enum.map(results, fn result -> result.performance_metrics[metric_key] end)
    {Enum.min(values), Enum.max(values)}
  end

  defp extract_novel_characteristics(_highly_novel) do
    # Placeholder - would extract what makes these experiments novel
    ["creative_combinations", "unexpected_insights", "breakthrough_approaches"]
  end

  defp calculate_average_execution_time(results) do
    if length(results) > 0 do
      results
      |> Enum.map(& &1.performance_metrics.execution_time)
      |> Enum.sum()
      |> Kernel./(length(results))
    else
      0.0
    end
  end

  defp analyze_resource_efficiency(_results) do
    # Placeholder - would analyze resource usage patterns
    :improving
  end

  defp analyze_time_quality_tradeoff(_results) do
    # Placeholder - would analyze tradeoff between time and quality
    %{correlation: 0.3, optimal_time_range: {1000, 5000}}
  end

  defp maybe_update_batch_size(settings, _insights) do
    # Placeholder - would adjust batch size based on insights
    settings
  end

  defp maybe_update_time_budget(settings, _insights) do
    # Placeholder - would adjust time budget based on insights
    settings
  end

  defp maybe_update_novelty_requirement(settings, _insights) do
    # Placeholder - would adjust novelty requirements based on insights
    settings
  end
end
