Add 5 new statistical test plugins with comprehensive tests

Co-authored-by: EdgeTypE <34396598+EdgeTypE@users.noreply.github.com>

Author: Copilot

patternanalyzer/plugins/chi_square.py

@@ -0,0 +1,236 @@
+"""Chi-Square test plugin for uniformity of byte distribution.
+
+The Chi-Square test checks whether the observed byte frequency distribution
+significantly deviates from the expected uniform distribution. A low p-value
+indicates the data is non-random or biased.
+"""
+
+import math
+from collections import Counter
+from typing import Dict, Any
+
+try:
+    from ..plugin_api import BytesView, TestResult, TestPlugin
+except Exception:
+    from patternanalyzer.plugin_api import BytesView, TestResult, TestPlugin  # type: ignore
+
+
+class ChiSquareTest(TestPlugin):
+    """Chi-Square test for byte frequency uniformity."""
+
+    def __init__(self):
+        """Initialize the plugin with streaming state."""
+        # Streaming accumulators
+        self._counter = Counter()
+        self._total_bytes = 0
+
+    def describe(self) -> str:
+        """Return plugin description."""
+        return "Chi-Square test for uniformity of byte distribution"
+
+    def run(self, data: BytesView, params: dict) -> TestResult:
+        """Run Chi-Square test in batch mode."""
+        data_bytes = data.to_bytes()
+        n = len(data_bytes)
+
+        if n == 0:
+            return TestResult(
+                test_name="chi_square",
+                passed=True,
+                p_value=1.0,
+                category="statistical",
+                metrics={"total_bytes": 0, "chi_square_statistic": 0.0},
+            )
+
+        # Count frequency of each byte value
+        counter = Counter(data_bytes)
+        
+        # Expected frequency for uniform distribution
+        expected = n / 256.0
+        
+        # Calculate chi-square statistic
+        chi_square = sum((count - expected) ** 2 / expected for count in counter.values())
+        
+        # Add missing byte values (count = 0) to chi-square
+        observed_bytes = len(counter)
+        missing_bytes = 256 - observed_bytes
+        if missing_bytes > 0:
+            chi_square += missing_bytes * (expected ** 2 / expected)
+        
+        # Degrees of freedom = 256 - 1 = 255
+        df = 255
+        
+        # Calculate p-value using chi-square CDF
+        p_value = 1.0 - self._chi_square_cdf(chi_square, df)
+        
+        # Determine if test passed
+        alpha = float(params.get("alpha", 0.01))
+        passed = p_value > alpha
+
+        return TestResult(
+            test_name="chi_square",
+            passed=passed,
+            p_value=p_value,
+            category="statistical",
+            metrics={
+                "total_bytes": n,
+                "chi_square_statistic": chi_square,
+                "degrees_of_freedom": df,
+                "unique_bytes": observed_bytes,
+            },
+            p_values={"chi_square": p_value},
+        )
+
+    def update(self, chunk: bytes, params: dict) -> None:
+        """Update internal accumulators with a chunk of raw bytes."""
+        if not chunk:
+            return
+        self._counter.update(chunk)
+        self._total_bytes += len(chunk)
+
+    def finalize(self, params: dict) -> TestResult:
+        """Finalize streaming aggregation and return TestResult."""
+        n = self._total_bytes
+        counter = self._counter
+        
+        # Reset accumulators for possible reuse
+        self._counter = Counter()
+        self._total_bytes = 0
+
+        if n == 0:
+            return TestResult(
+                test_name="chi_square",
+                passed=True,
+                p_value=1.0,
+                category="statistical",
+                metrics={"total_bytes": 0, "chi_square_statistic": 0.0},
+            )
+
+        # Expected frequency for uniform distribution
+        expected = n / 256.0
+        
+        # Calculate chi-square statistic
+        chi_square = sum((count - expected) ** 2 / expected for count in counter.values())
+        
+        # Add missing byte values (count = 0) to chi-square
+        observed_bytes = len(counter)
+        missing_bytes = 256 - observed_bytes
+        if missing_bytes > 0:
+            chi_square += missing_bytes * (expected ** 2 / expected)
+        
+        # Degrees of freedom = 256 - 1 = 255
+        df = 255
+        
+        # Calculate p-value using chi-square CDF
+        p_value = 1.0 - self._chi_square_cdf(chi_square, df)
+        
+        # Determine if test passed
+        alpha = float(params.get("alpha", 0.01))
+        passed = p_value > alpha
+
+        return TestResult(
+            test_name="chi_square",
+            passed=passed,
+            p_value=p_value,
+            category="statistical",
+            metrics={
+                "total_bytes": n,
+                "chi_square_statistic": chi_square,
+                "degrees_of_freedom": df,
+                "unique_bytes": observed_bytes,
+            },
+            p_values={"chi_square": p_value},
+        )
+
+    def _chi_square_cdf(self, x: float, df: int) -> float:
+        """Approximate chi-square cumulative distribution function.
+        
+        Uses the relationship between chi-square and gamma distribution.
+        For large df, uses normal approximation.
+        """
+        if x <= 0:
+            return 0.0
+        
+        if df > 100:
+            # Wilson-Hilferty transformation for large df
+            z = ((x / df) ** (1.0/3.0) - (1.0 - 2.0/(9.0*df))) / math.sqrt(2.0/(9.0*df))
+            return self._normal_cdf(z)
+        
+        # Use incomplete gamma function for small to medium df
+        return self._gamma_cdf(x / 2.0, df / 2.0)
+
+    def _gamma_cdf(self, x: float, k: float) -> float:
+        """Approximate gamma CDF using incomplete gamma function."""
+        if x <= 0:
+            return 0.0
+        
+        # Use series expansion for small x*k, continued fraction for large x*k
+        if x * k < 1.0:
+            # Series expansion
+            return self._gamma_series(x, k)
+        else:
+            # Continued fraction
+            return 1.0 - self._gamma_cf(x, k)
+
+    def _gamma_series(self, x: float, k: float) -> float:
+        """Series expansion for lower incomplete gamma."""
+        max_iter = 1000
+        epsilon = 1e-10
+        
+        result = 1.0 / k
+        term = result
+        
+        for n in range(1, max_iter):
+            term *= x / (k + n)
+            result += term
+            if abs(term) < epsilon:
+                break
+        
+        return result * math.exp(-x + k * math.log(x) - math.lgamma(k))
+
+    def _gamma_cf(self, x: float, k: float) -> float:
+        """Continued fraction for upper incomplete gamma."""
+        max_iter = 1000
+        epsilon = 1e-10
+        
+        # Lentz's algorithm
+        tiny = 1e-30
+        b = x + 1.0 - k
+        c = 1.0 / tiny
+        d = 1.0 / b
+        h = d
+        
+        for i in range(1, max_iter):
+            a = -i * (i - k)
+            b += 2.0
+            d = a * d + b
+            if abs(d) < tiny:
+                d = tiny
+            c = b + a / c
+            if abs(c) < tiny:
+                c = tiny
+            d = 1.0 / d
+            delta = d * c
+            h *= delta
+            if abs(delta - 1.0) < epsilon:
+                break
+        
+        return h * math.exp(-x + k * math.log(x) - math.lgamma(k))
+
+    def _normal_cdf(self, x: float) -> float:
+        """Approximation of standard normal cumulative distribution function."""
+        # Abramowitz and Stegun approximation
+        a1 = 0.254829592
+        a2 = -0.284496736
+        a3 = 1.421413741
+        a4 = -1.453152027
+        a5 = 1.061405429
+        p = 0.3275911
+
+        sign = 1 if x >= 0 else -1
+        x = abs(x) / math.sqrt(2.0)
+
+        t = 1.0 / (1.0 + p * x)
+        y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * math.exp(-x * x)
+
+        return 0.5 * (1.0 + sign * y)