#!/bin/bash
#===============================================================================
# active_learner.sh - Active Learning with Information-Theoretic Selection
#
# Implements:
#   - Information gain computation for topic selection
#   - Uncertainty sampling
#   - Curiosity-driven exploration with UCB
#   - Spaced repetition (SM-2 algorithm)
#   - Knowledge graph integration
#===============================================================================

set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
source "${SCRIPT_DIR}/../orchestrator/lib/supervisor.sh" 2>/dev/null || true

#===============================================================================
# CONFIGURATION
#===============================================================================

LEARNER_DB="${LEARNER_DB:-${SCRIPT_DIR}/knowledge_graph.db}"
LEARNER_STATE="${LEARNER_STATE:-${SCRIPT_DIR}/learner_state.json}"
LEARNER_LOG="${LEARNER_LOG:-${SCRIPT_DIR}/learner.log}"

# Learning parameters (golden ratio scaled)
PHI=1.6180339887
ALPHA=$(echo "scale=4; 0.1 * $PHI" | bc)
BETA=$(echo "scale=4; 0.05 * $PHI" | bc)
GAMMA=$(echo "scale=4; 0.02 * $PHI" | bc)

# UCB parameters
UCB_C=2.0  # Exploration constant

# Spaced repetition (SM-2)
SM2_INITIAL_INTERVAL=1  # days
SM2_INITIAL_EF=2.5

#===============================================================================
# KNOWLEDGE CATEGORIES AND SOURCES
#===============================================================================

declare -A CATEGORIES=(
    [science]="physics chemistry biology astronomy neuroscience"
    [technology]="ai ml programming systems networks"
    [philosophy]="ethics metaphysics epistemology consciousness"
    [mathematics]="algebra calculus topology statistics"
    [arts]="music literature visual_arts"
    [history]="ancient modern contemporary"
)

declare -A SOURCES=(
    [wikipedia]="https://en.wikipedia.org/api/rest_v1/page/random/summary"
    [arxiv]="http://export.arxiv.org/api/query?search_query=cat:cs.AI&max_results=1&sortBy=submittedDate"
    [semantic_scholar]="https://api.semanticscholar.org/graph/v1/paper/search?query="
)

#===============================================================================
# DATABASE OPERATIONS
#===============================================================================

db_init() {
    sqlite3 "$LEARNER_DB" <<'SQL'
    CREATE TABLE IF NOT EXISTS concepts (
        id INTEGER PRIMARY KEY,
        concept TEXT UNIQUE,
        domain TEXT,
        category TEXT,
        confidence REAL DEFAULT 0.5,
        uncertainty REAL DEFAULT 1.0,
        access_count INTEGER DEFAULT 0,
        info_gain REAL DEFAULT 1.0,
        created_at TEXT DEFAULT (datetime('now')),
        last_accessed TEXT
    );

    CREATE TABLE IF NOT EXISTS repetition (
        concept_id INTEGER PRIMARY KEY,
        repetition INTEGER DEFAULT 0,
        easiness REAL DEFAULT 2.5,
        interval REAL DEFAULT 1,
        next_review TEXT,
        last_review TEXT,
        last_quality INTEGER DEFAULT 3,
        FOREIGN KEY (concept_id) REFERENCES concepts(id)
    );

    CREATE TABLE IF NOT EXISTS learning_history (
        id INTEGER PRIMARY KEY,
        concept_id INTEGER,
        action TEXT,
        reward REAL,
        timestamp TEXT DEFAULT (datetime('now')),
        FOREIGN KEY (concept_id) REFERENCES concepts(id)
    );

    CREATE INDEX IF NOT EXISTS idx_concepts_domain ON concepts(domain);
    CREATE INDEX IF NOT EXISTS idx_concepts_uncertainty ON concepts(uncertainty DESC);
    CREATE INDEX IF NOT EXISTS idx_repetition_next ON repetition(next_review);
SQL
}

db_query() {
    sqlite3 -separator '|' "$LEARNER_DB" "$1"
}

#===============================================================================
# INFORMATION GAIN COMPUTATION
#===============================================================================

# Compute information gain for learning a new concept
compute_info_gain() {
    local concept="$1"
    local domain="$2"

    # Check if already known
    local existing=$(db_query "SELECT confidence FROM concepts WHERE concept='$concept' LIMIT 1")
    if [[ -n "$existing" ]]; then
        # Low gain for already-known concepts
        echo "0.1"
        return
    fi

    # Get domain statistics
    local domain_count=$(db_query "SELECT COUNT(*) FROM concepts WHERE domain='$domain'")
    local domain_avg_conf=$(db_query "SELECT COALESCE(AVG(confidence), 0.5) FROM concepts WHERE domain='$domain'")

    if [[ -z "$domain_count" ]] || [[ "$domain_count" == "0" ]]; then
        # New domain = high information gain
        echo "1.0"
        return
    fi

    # Information gain = entropy reduction
    # More uncertain domains have higher gain
    local uncertainty=$(echo "scale=4; 1 - $domain_avg_conf" | bc)

    # Bonus for connecting to existing knowledge
    local connectivity_bonus=$(echo "scale=4; l($domain_count + 1) / 10" | bc -l 2>/dev/null || echo "0.1")

    local gain=$(echo "scale=4; $uncertainty * (1 + $connectivity_bonus)" | bc)

    # Clamp to [0, 1]
    if (( $(echo "$gain > 1" | bc -l) )); then
        gain="1.0"
    fi

    echo "$gain"
}

#===============================================================================
# UCB (Upper Confidence Bound) SELECTION
#===============================================================================

# UCB score: exploitation + exploration
compute_ucb_score() {
    local concept_id="$1"
    local total_selections="$2"

    # Get concept statistics
    local stats=$(db_query "SELECT confidence, access_count, info_gain FROM concepts WHERE id=$concept_id")
    local confidence=$(echo "$stats" | cut -d'|' -f1)
    local access_count=$(echo "$stats" | cut -d'|' -f2)
    local info_gain=$(echo "$stats" | cut -d'|' -f3)

    # Exploitation: confidence * info_gain
    local exploitation=$(echo "scale=4; $confidence * $info_gain" | bc)

    # Exploration: sqrt(2 * ln(t) / n)
    local exploration=1.0
    if [[ "$access_count" -gt 0 ]]; then
        exploration=$(echo "scale=4; sqrt($UCB_C * l($total_selections + 1) / $access_count)" | bc -l 2>/dev/null || echo "1")
    fi

    # UCB score
    echo "scale=4; $exploitation + $exploration" | bc
}

# Select best concept using UCB
select_concept_ucb() {
    local domain="${1:-}"
    local limit="${2:-10}"

    local where_clause=""
    if [[ -n "$domain" ]]; then
        where_clause="WHERE domain='$domain'"
    fi

    # Get total selections
    local total=$(db_query "SELECT COUNT(*) FROM learning_history")

    # Get candidate concepts
    local candidates=$(db_query "SELECT id, concept, uncertainty FROM concepts $where_clause ORDER BY uncertainty DESC LIMIT $limit")

    local best_id=""
    local best_score="-999"

    while IFS='|' read -r id concept uncertainty; do
        [[ -z "$id" ]] && continue

        local score=$(compute_ucb_score "$id" "$total")

        if (( $(echo "$score > $best_score" | bc -l) )); then
            best_score="$score"
            best_id="$id"
        fi
    done <<< "$candidates"

    echo "$best_id"
}

#===============================================================================
# SPACED REPETITION (SM-2 ALGORITHM)
#===============================================================================

# Get concepts due for review
get_due_for_review() {
    local limit="${1:-10}"

    db_query "
        SELECT c.id, c.concept, c.domain, r.repetition, r.easiness
        FROM concepts c
        JOIN repetition r ON c.id = r.concept_id
        WHERE r.next_review <= datetime('now')
        ORDER BY r.next_review ASC
        LIMIT $limit
    "
}

# Record review result and update schedule
record_review() {
    local concept_id="$1"
    local quality="$2"  # 0-5

    # Get current state
    local state=$(db_query "SELECT repetition, easiness, interval FROM repetition WHERE concept_id=$concept_id")
    local rep=$(echo "$state" | cut -d'|' -f1)
    local ef=$(echo "$state" | cut -d'|' -f2)
    local interval=$(echo "$state" | cut -d'|' -f3)

    # SM-2 algorithm
    if [[ "$quality" -lt 3 ]]; then
        # Failed: reset
        rep=0
        interval=1
    else
        # Success
        ((rep++))
        if [[ "$rep" -eq 1 ]]; then
            interval=1
        elif [[ "$rep" -eq 2 ]]; then
            interval=6
        else
            interval=$(echo "scale=1; $interval * $ef" | bc)
        fi
    fi

    # Update easiness factor
    local ef_delta=$(echo "scale=4; 0.1 - (5 - $quality) * (0.08 + (5 - $quality) * 0.02)" | bc)
    ef=$(echo "scale=4; $ef + $ef_delta" | bc)
    if (( $(echo "$ef < 1.3" | bc -l) )); then
        ef="1.3"
    fi

    # Calculate next review date
    local next_review=$(date -d "+${interval%.*} days" '+%Y-%m-%d %H:%M:%S' 2>/dev/null || \
                        date -v "+${interval%.*}d" '+%Y-%m-%d %H:%M:%S' 2>/dev/null || \
                        echo "$(date '+%Y-%m-%d %H:%M:%S')")

    # Update database
    db_query "
        UPDATE repetition SET
            repetition = $rep,
            easiness = $ef,
            interval = $interval,
            next_review = '$next_review',
            last_review = datetime('now'),
            last_quality = $quality
        WHERE concept_id = $concept_id
    "

    # Update concept confidence
    if [[ "$quality" -ge 3 ]]; then
        local conf_boost=$(echo "scale=4; $ALPHA * $quality / 5" | bc)
        db_query "UPDATE concepts SET confidence = MIN(1.0, confidence + $conf_boost * (1 - confidence)) WHERE id = $concept_id"
    else
        db_query "UPDATE concepts SET confidence = MAX(0.1, confidence * (1 - $GAMMA)) WHERE id = $concept_id"
    fi

    # Log
    db_query "INSERT INTO learning_history (concept_id, action, reward) VALUES ($concept_id, 'review', $quality)"

    echo "Updated: rep=$rep, ef=$ef, interval=$interval, next=$next_review"
}

#===============================================================================
# ACTIVE LEARNING STRATEGIES
#===============================================================================

# Strategy: EXPLORE - Random exploration
strategy_explore() {
    local category="${1:-science}"
    local topics="${CATEGORIES[$category]:-physics}"

    # Pick random topic from category
    local topic=$(echo "$topics" | tr ' ' '\n' | shuf -n 1)

    echo "EXPLORE: Fetching from $topic in $category"

    # Fetch from Wikipedia
    local result=$(curl -s "${SOURCES[wikipedia]}" 2>/dev/null | jq -r '.title // empty')

    if [[ -n "$result" ]]; then
        local gain=$(compute_info_gain "$result" "$category")
        echo "Learned: $result (gain: $gain)"

        # Add to database
        db_query "INSERT OR IGNORE INTO concepts (concept, domain, category, info_gain) VALUES ('$result', '$category', '$topic', $gain)"
        local concept_id=$(db_query "SELECT id FROM concepts WHERE concept='$result'")
        db_query "INSERT OR IGNORE INTO repetition (concept_id, next_review) VALUES ($concept_id, datetime('now', '+1 day'))"
    fi
}

# Strategy: UNCERTAINTY - Sample most uncertain concepts
strategy_uncertainty() {
    echo "UNCERTAINTY: Selecting most uncertain concept"

    local uncertain=$(db_query "SELECT id, concept, uncertainty FROM concepts ORDER BY uncertainty DESC LIMIT 1")
    if [[ -n "$uncertain" ]]; then
        local id=$(echo "$uncertain" | cut -d'|' -f1)
        local concept=$(echo "$uncertain" | cut -d'|' -f2)

        echo "Reinforcing: $concept"

        # Simulate learning (reduce uncertainty)
        db_query "UPDATE concepts SET uncertainty = uncertainty * 0.9, access_count = access_count + 1 WHERE id = $id"
        db_query "INSERT INTO learning_history (concept_id, action, reward) VALUES ($id, 'uncertainty', 0.1)"
    fi
}

# Strategy: UCB - Upper Confidence Bound selection
strategy_ucb() {
    local domain="${1:-}"

    echo "UCB: Selecting by exploration-exploitation balance"

    local concept_id=$(select_concept_ucb "$domain")
    if [[ -n "$concept_id" ]]; then
        local concept=$(db_query "SELECT concept FROM concepts WHERE id=$concept_id")
        echo "Selected: $concept (id: $concept_id)"

        db_query "UPDATE concepts SET access_count = access_count + 1, last_accessed = datetime('now') WHERE id = $concept_id"
        db_query "INSERT INTO learning_history (concept_id, action, reward) VALUES ($concept_id, 'ucb', 0.5)"
    fi
}

# Strategy: REINFORCE - Review due concepts
strategy_reinforce() {
    echo "REINFORCE: Reviewing due concepts"

    local due=$(get_due_for_review 5)
    if [[ -z "$due" ]]; then
        echo "No concepts due for review"
        return
    fi

    while IFS='|' read -r id concept domain rep ef; do
        [[ -z "$id" ]] && continue

        echo "Reviewing: $concept (repetition: $rep)"

        # Simulate review (random quality 3-5 for now)
        local quality=$((3 + RANDOM % 3))
        record_review "$id" "$quality"
    done <<< "$due"
}

#===============================================================================
# MAIN LEARNING LOOP
#===============================================================================

# Initialize if needed
init_learner() {
    db_init

    # Initialize state file
    if [[ ! -f "$LEARNER_STATE" ]]; then
        cat > "$LEARNER_STATE" <<EOF
{
    "total_ticks": 0,
    "total_learned": 0,
    "current_strategy": "explore",
    "phi": 0.5,
    "strategies": ["explore", "uncertainty", "ucb", "reinforce"],
    "strategy_weights": [0.3, 0.2, 0.3, 0.2]
}
EOF
    fi
}

# Select strategy based on weights
select_strategy() {
    local strategies=("explore" "uncertainty" "ucb" "reinforce")
    local weights=(30 20 30 20)  # Percentage weights

    local total=100
    local rand=$((RANDOM % total))
    local cumulative=0

    for i in "${!strategies[@]}"; do
        cumulative=$((cumulative + weights[i]))
        if [[ $rand -lt $cumulative ]]; then
            echo "${strategies[$i]}"
            return
        fi
    done

    echo "explore"
}

# Main learning tick
learning_tick() {
    local strategy=$(select_strategy)

    case "$strategy" in
        explore)
            local categories=("science" "technology" "philosophy" "mathematics")
            local category="${categories[$((RANDOM % ${#categories[@]}))]}"
            strategy_explore "$category"
            ;;
        uncertainty)
            strategy_uncertainty
            ;;
        ucb)
            strategy_ucb
            ;;
        reinforce)
            strategy_reinforce
            ;;
    esac

    # Update state
    local total_concepts=$(db_query "SELECT COUNT(*) FROM concepts")
    local avg_confidence=$(db_query "SELECT COALESCE(AVG(confidence), 0.5) FROM concepts")

    # Compute phi
    local phi=$(echo "scale=4; 1 / (1 + sqrt((($avg_confidence) - 1)^2 + 0.01))" | bc -l 2>/dev/null || echo "0.5")

    # Update state file
    if command -v jq &>/dev/null; then
        local ticks=$(jq -r '.total_ticks' "$LEARNER_STATE")
        ((ticks++))
        jq --arg ticks "$ticks" \
           --arg learned "$total_concepts" \
           --arg strategy "$strategy" \
           --arg phi "$phi" \
           '.total_ticks = ($ticks | tonumber) |
            .total_learned = ($learned | tonumber) |
            .current_strategy = $strategy |
            .phi = ($phi | tonumber)' \
           "$LEARNER_STATE" > "${LEARNER_STATE}.tmp" && mv "${LEARNER_STATE}.tmp" "$LEARNER_STATE"
    fi

    echo "Tick complete: strategy=$strategy, concepts=$total_concepts, phi=$phi"
}

# Run learning daemon
run_learner_daemon() {
    init_learner

    echo "Starting Active Learner Daemon"
    echo "Database: $LEARNER_DB"
    echo "State: $LEARNER_STATE"

    while true; do
        learning_tick
        sleep 60  # 1 tick per minute
    done
}

#===============================================================================
# CLI
#===============================================================================

case "${1:-}" in
    init)
        init_learner
        echo "Initialized active learner"
        ;;
    tick)
        init_learner
        learning_tick
        ;;
    explore)
        init_learner
        strategy_explore "${2:-science}"
        ;;
    review)
        init_learner
        strategy_reinforce
        ;;
    status)
        init_learner
        echo "=== Active Learner Status ==="
        echo "Total concepts: $(db_query 'SELECT COUNT(*) FROM concepts')"
        echo "Avg confidence: $(db_query 'SELECT COALESCE(AVG(confidence), 0) FROM concepts')"
        echo "Due for review: $(db_query 'SELECT COUNT(*) FROM repetition WHERE next_review <= datetime("now")')"
        if [[ -f "$LEARNER_STATE" ]]; then
            echo "State: $(cat "$LEARNER_STATE")"
        fi
        ;;
    daemon)
        run_learner_daemon
        ;;
    *)
        echo "Usage: $0 {init|tick|explore|review|status|daemon}"
        ;;
esac