From 07e18043145f3d91c8ea7ec3ad41b3c905585277 Mon Sep 17 00:00:00 2001
From: Ethan Werner <mail@ethanwerner.com>
Date: Wed, 1 Jul 2026 09:42:39 +0400
Subject: initial commit: ts time series analysis library v0.1.0

- 354 financial and mathematical time-series indicators
- Uniform C11 API with consistent function signatures
- 349 passing tests with -Werror
- Static and shared library builds
- pkg-config integration
- Python bindings
- Fuzzing and sanitizer support
- Security hardening flags
---
 lib/binomial_pdf.c | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 lib/binomial_pdf.c

(limited to 'lib/binomial_pdf.c')

diff --git a/lib/binomial_pdf.c b/lib/binomial_pdf.c
new file mode 100644
index 0000000..8b3a442
--- /dev/null
+++ b/lib/binomial_pdf.c
@@ -0,0 +1,63 @@
+// Binomial PDF
+//
+// binomial_pdf - binomial probability density
+//
+// P(X=k) = C(n, k) * p^k * (1-p)^(n-k)
+//
+// Inputs:    1 (k values)
+// Outputs:   1 (PDF value)
+// Constants: 1 (n trials, p probability)
+
+#include <math.h>
+#include "ts.h"
+
+typedef struct {
+    uint_t n_trials;
+    fp_t p;
+} ts_binomial_pdf_params_t;
+
+ts_parameter_t const ts_binomial_pdf_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 1, .params_size = sizeof(ts_binomial_pdf_params_t) };
+
+uint_t ts_binomial_pdf_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( 0 );
+}
+
+int ts_binomial_pdf(
+	fp_t * const *y, uint_t y_n,
+	fp_t const * const *x, uint_t x_n,
+	uint_t n, ts_constant_t const *c
+)
+{
+	TS_PROLOGUE( ts_binomial_pdf_p );
+	if ( !c ) return -1;
+	uint_t n_trials = ((ts_binomial_pdf_params_t *)c)->n_trials;
+	fp_t p = ((ts_binomial_pdf_params_t *)c)->p;
+	if (p < 0.0 || p > 1.0 || isnan(p) || isinf(p)) return -1;
+	for( uint_t i = 0; i < n; ++i )
+	{
+		uint_t k = (uint_t)x[0][i];
+		if( k > n_trials )
+		{
+			y[0][i] = 0.0;
+		}
+		else if( p == 0.0 )
+		{
+			y[0][i] = k == 0 ? 1.0 : 0.0;
+		}
+		else if( p == 1.0 )
+		{
+			y[0][i] = k == n_trials ? 1.0 : 0.0;
+		}
+		else
+		{
+			fp_t log_comb = lgamma( n_trials + 1.0 ) - lgamma( k + 1.0 ) - lgamma( n_trials - k + 1.0 );
+			y[0][i] = exp( log_comb + k * log( p ) + ( n_trials - k ) * log( 1.0 - p ) );
+		}
+	}
+	return 0;
+}
+
+
+
-- 
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