开始设计

cKit/Math/c_LinearRegression.h

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+#ifndef INCLUDED_C_LINEARREGRESSION_H
+#define INCLUDED_C_LINEARREGRESSION_H
+
+#ifndef INCLUDED_C_BASE_H
+#include <c_Base.h>
+#endif /*INCLUDED_C_BASE_H*/
+
+
+/* ------------------------------------------------------------------------------------------------------------------ */
+/*  */
+
+// Linear Regression Model Context Structure
+typedef struct {
+    double slope;       // Slope coefficient (Beta)
+    double intercept;   // Y-Intercept coefficient (Alpha)
+    c_bool_t is_trained;// State flag tracking model training status
+} c_LinearRegression_t;
+
+/* ------------------------------------------------------------------------------------------------------------------ */
+/*  */
+
+
+/**
+ * Initialize the Linear Regression Model context block.
+ */
+C_STATIC_FORCE_INLINE
+c_err_t c_LinearRegression_Init(c_LinearRegression_t* model) {
+    if (model == NULL) return C_ERR_PARAM;
+
+    model->slope = 0.0;
+    model->intercept = 0.0;
+    model->is_trained = C_FALSE;
+
+    return C_ERR_OK;
+}
+
+/**
+ * Train the model using Ordinary Least Squares (OLS) linear derivation math.
+ * Time Complexity: O(N) | Auxiliary Space: O(1) in-place
+ * @param model  Pointer to the linear regression model context instance.
+ * @param x      Contiguous array tracking independent variable observations.
+ * @param y      Contiguous array tracking dependent variable target features.
+ * @param num    Total number of data points inside the training array sets.
+ * @return       C_ERR_OK if successful, C_ERR_PARAM for NULL targets,
+ *               or C_ERR_INVALID if variance evaluates to zero (vertical line slope anomaly).
+ */
+C_STATIC_FORCE_INLINE
+c_err_t c_LinearRegression_Fit(c_LinearRegression_t* model, const double* x, const double* y, c_size_t num) {
+    if (model == NULL || x == NULL || y == NULL) return C_ERR_PARAM;
+    if (num < 2) return C_ERR_PARAM; // Mandate at least two distinct points to draw a trend line
+
+    double sum_x = 0.0;
+    double sum_y = 0.0;
+
+    // Step 1: Calculate the arithmetic mean values for features X and Y
+    for (c_size_t i = 0; i < num; i++) {
+        sum_x += x[i];
+        sum_y += y[i];
+    }
+    double mean_x = sum_x / (double)num;
+    double mean_y = sum_y / (double)num;
+
+    double num_covariance = 0.0;
+    double den_variance = 0.0;
+
+    // Step 2: Accumulate sample covariance and independent feature variance maps
+    for (c_size_t i = 0; i < num; i++) {
+        double diff_x = x[i] - mean_x;
+        num_covariance += diff_x * (y[i] - mean_y);
+        den_variance += diff_x * diff_x;
+    }
+
+    // Step 3: Guard against division-by-zero on perfectly vertical data layouts
+    if (den_variance == 0.0) {
+        model->is_trained = C_FALSE;
+        return C_ERR_INVALID;
+    }
+
+    // Step 4: Map final slope and intercept boundary coefficients
+    model->slope = num_covariance / den_variance;
+    model->intercept = mean_y - (model->slope * mean_x);
+    model->is_trained = C_TRUE;
+
+    return C_ERR_OK;
+}
+
+/**
+ * Inference Lookahead: Predict the output target value for a specific input feature.
+ * @param model     Pointer to the constant trained model instance.
+ * @param x         The input independent scalar variable point.
+ * @param out_val   Pointer to the destination variable where the predicted Y value is written.
+ * @return          C_ERR_OK if successful, or C_ERR_INVALID if the model is untrained.
+ */
+C_STATIC_FORCE_INLINE
+c_err_t c_LinearRegression_Predict(const c_LinearRegression_t* model, double x, double* out_val) {
+    if (model == NULL || out_val == NULL) return C_ERR_PARAM;
+    if (!model->is_trained) return C_ERR_INVALID;
+
+    // Execute standard linear function lookup: y = alpha + beta * x
+    *out_val = model->intercept + (model->slope * x);
+    return C_ERR_OK;
+}
+
+
+
+#endif /*INCLUDED_C_LINEARREGRESSION_H*/