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import joblib
import numpy as np
def _get_scaler_and_classes(eng):
model_dir = getattr(eng, 'model_dir', None)
if not model_dir:
from preprocessing import resolve_model_dir
model_dir = resolve_model_dir()
means = [85.0, 96.67, 125.0, 85.0, 37.2, 0.0]
scales = [4.08, 1.25, 4.08, 4.08, 0.21, 1.0]
class_names = ["Class_0", "Class_1"]
if os.path.exists(os.path.join(model_dir, 'scaler.pkl')):
try:
scaler = joblib.load(os.path.join(model_dir, 'scaler.pkl'))
means = [float(v) for v in scaler.mean_]
scales = [float(v) for v in scaler.scale_]
except Exception:
pass
if os.path.exists(os.path.join(model_dir, 'classes.pkl')):
try:
classes = joblib.load(os.path.join(model_dir, 'classes.pkl'))
class_names = [str(c) for c in classes]
except Exception:
pass
elif hasattr(eng, 'label_encoder') and eng.label_encoder is not None:
try:
class_names = [str(c) for c in eng.label_encoder.classes_]
except Exception:
pass
return means, scales, class_names
def _emit_tree_ast_c(tree, node_id, indent=" ", quantize=False, scale_factor=16.0):
lines = []
if tree.children_left[node_id] == -1:
# Leaf node: return argmax class
class_idx = int(np.argmax(tree.value[node_id][0]))
lines.append(f"{indent}return {class_idx};")
return lines
feat = int(tree.feature[node_id])
thresh = float(tree.threshold[node_id])
if quantize:
thresh_q = int(round(thresh * scale_factor))
lines.append(f"{indent}if (f[{feat}] <= {thresh_q}) {{")
else:
lines.append(f"{indent}if (f[{feat}] <= {thresh:.6f}) {{")
lines.extend(_emit_tree_ast_c(tree, tree.children_left[node_id], indent + " ", quantize, scale_factor))
lines.append(f"{indent}}} else {{")
lines.extend(_emit_tree_ast_c(tree, tree.children_right[node_id], indent + " ", quantize, scale_factor))
lines.append(f"{indent}}}")
return lines
def generate_c_code(eng, model_name: str, quantize: bool):
if not eng or model_name not in eng.models:
return {"error": f"Model {model_name} not found"}
model = eng.models[model_name]
means, scales, class_names = _get_scaler_and_classes(eng)
n_classes = len(class_names)
q_text = "INT8 Quantized" if quantize else "FP32 Double"
L = []
L.append("/* ========================================================================== */")
L.append(f"/* TinyML C Header Export: {model_name.upper()} ({q_text}) */")
L.append("/* Generated by HEARTFLOW_OS Edge Compiler Toolchain */")
L.append("/* Zero dynamic heap memory (0 malloc bytes) // Bare-Metal Ready */")
L.append("/* ========================================================================== */")
L.append("")
L.append("#ifndef HEARTFLOW_MODEL_H")
L.append("#define HEARTFLOW_MODEL_H")
L.append("")
L.append("#include <stdint.h>")
L.append("#include <math.h>")
L.append("#include <string.h>")
L.append("")
L.append(f"#define N_CLASSES {n_classes}")
L.append(f"#define N_RAW_FEATURES 5")
L.append(f"#define N_SCALED_FEATURES 6")
L.append("")
# Class names string array
c_names_str = ", ".join([f'"{name}"' for name in class_names])
L.append(f"static const char *CLASS_NAMES[N_CLASSES] = {{{c_names_str}}};")
L.append("")
# Preprocessing constants (StandardScaler parameters)
means_str = ", ".join([f"{m:.6f}" for m in means])
scales_str = ", ".join([f"{s:.6f}" for s in scales])
L.append(f"static const double SCALER_MEAN[N_SCALED_FEATURES] = {{{means_str}}};")
L.append(f"static const double SCALER_SCALE[N_SCALED_FEATURES] = {{{scales_str}}};")
L.append("")
# Preprocessing helpers
L.append("/* Normalizes 5 raw telemetry vitals and appends engineered Risk_Severity */")
L.append("static inline void preprocess_vitals(const float *raw, double *out) {")
L.append(" for (int i = 0; i < 5; i++) {")
L.append(" out[i] = ((double)raw[i] - SCALER_MEAN[i]) / SCALER_SCALE[i];")
L.append(" }")
L.append(" double risk_sev = (raw[0] > 105.0f ? 1.0 : 0.0) + (raw[1] < 94.0f ? 1.0 : 0.0);")
L.append(" out[5] = (risk_sev - SCALER_MEAN[5]) / SCALER_SCALE[5];")
L.append("}")
L.append("")
if quantize:
L.append("/* Quantizes continuous features to INT8 fixed-point (scale factor: 16.0) */")
L.append("static inline void preprocess_vitals_int8(const float *raw, int8_t *out) {")
L.append(" double norm[6];")
L.append(" preprocess_vitals(raw, norm);")
L.append(" for (int i = 0; i < 6; i++) {")
L.append(" int32_t q = (int32_t)round(norm[i] * 16.0);")
L.append(" if (q > 127) q = 127;")
L.append(" if (q < -128) q = -128;")
L.append(" out[i] = (int8_t)q;")
L.append(" }")
L.append("}")
L.append("")
# 1. RANDOM FOREST
if model_name == "rf":
n_trees = len(model.estimators_)
L.append(f"#define N_TREES {n_trees}")
L.append("")
if quantize:
# Fixed-point decision trees
for i, est in enumerate(model.estimators_):
L.append(f"static inline int tree_vote_{i}(const int8_t *f) {{")
L.extend(_emit_tree_ast_c(est.tree_, 0, indent=" ", quantize=True, scale_factor=16.0))
L.append("}")
L.append("")
L.append("static inline int predict_from_int8(const int8_t *features) {")
L.append(" uint16_t votes[N_CLASSES] = {0};")
for i in range(n_trees):
L.append(f" votes[tree_vote_{i}(features)]++;")
L.append(" int best = 0;")
L.append(" for (int c = 1; c < N_CLASSES; c++) {")
L.append(" if (votes[c] > votes[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
L.append("")
L.append("/* Primary inference API: expects 5 raw vitals [HR, SpO2, SysBP, DiaBP, Temp] */")
L.append("int predict(const float *features) {")
L.append(" int8_t q_features[6];")
L.append(" preprocess_vitals_int8(features, q_features);")
L.append(" return predict_from_int8(q_features);")
L.append("}")
else:
# FP32 decision trees
for i, est in enumerate(model.estimators_):
L.append(f"static inline int tree_vote_{i}(const double *f) {{")
L.extend(_emit_tree_ast_c(est.tree_, 0, indent=" ", quantize=False))
L.append("}")
L.append("")
L.append("static inline int predict_from_norm(const double *norm_features) {")
L.append(" uint16_t votes[N_CLASSES] = {0};")
for i in range(n_trees):
L.append(f" votes[tree_vote_{i}(norm_features)]++;")
L.append(" int best = 0;")
L.append(" for (int c = 1; c < N_CLASSES; c++) {")
L.append(" if (votes[c] > votes[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
L.append("")
L.append("/* Primary inference API: expects 5 raw vitals [HR, SpO2, SysBP, DiaBP, Temp] */")
L.append("int predict(const float *features) {")
L.append(" double norm[6];")
L.append(" preprocess_vitals(features, norm);")
L.append(" return predict_from_norm(norm);")
L.append("}")
# 2. LINEAR SVM
elif model_name == "svm" and hasattr(model, 'coef_'):
coefs = model.coef_
intercepts = model.intercept_
n_features = coefs.shape[1]
n_hyperplanes = coefs.shape[0]
L.append(f"#define N_FEATURES {n_features}")
L.append(f"#define N_HYPERPLANES {n_hyperplanes}")
L.append("")
if quantize:
scale_factor = 127.0 / max(float(np.max(np.abs(coefs))), float(np.max(np.abs(intercepts))), 1e-6)
L.append(f"/* Quantization Scale: {scale_factor:.4f} */")
L.append(f"static const int8_t SVM_COEF[N_HYPERPLANES][N_FEATURES] = {{")
for row in coefs:
vals = ", ".join([str(int(round(v * scale_factor))) for v in row])
L.append(f" {{{vals}}},")
L.append("};")
L.append("")
vals = ", ".join([str(int(round(v * scale_factor))) for v in intercepts])
L.append(f"static const int8_t SVM_INTERCEPT[N_HYPERPLANES] = {{{vals}}};")
L.append("")
L.append("int predict(const float *features) {")
L.append(" int8_t q_features[6];")
L.append(" preprocess_vitals_int8(features, q_features);")
L.append(" int32_t scores[N_CLASSES] = {0};")
L.append(" int h = 0;")
L.append(" for (int i = 0; i < N_CLASSES; i++) {")
L.append(" for (int j = i + 1; j < N_CLASSES; j++) {")
L.append(" int32_t decision = SVM_INTERCEPT[h];")
L.append(" for (int f = 0; f < N_FEATURES; f++) {")
L.append(" decision += (int32_t)SVM_COEF[h][f] * q_features[f];")
L.append(" }")
L.append(" if (decision > 0) scores[i] += 1;")
L.append(" else scores[j] += 1;")
L.append(" h++;")
L.append(" }")
L.append(" }")
L.append(" int best = 0;")
L.append(" for (int c = 1; c < N_CLASSES; c++) {")
L.append(" if (scores[c] > scores[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
else:
L.append(f"static const double SVM_COEF[N_HYPERPLANES][N_FEATURES] = {{")
for row in coefs:
vals = ", ".join([f"{v:.6f}" for v in row])
L.append(f" {{{vals}}},")
L.append("};")
L.append("")
vals = ", ".join([f"{v:.6f}" for v in intercepts])
L.append(f"static const double SVM_INTERCEPT[N_HYPERPLANES] = {{{vals}}};")
L.append("")
L.append("int predict(const float *features) {")
L.append(" double norm[6];")
L.append(" preprocess_vitals(features, norm);")
L.append(" double scores[N_CLASSES] = {0};")
L.append(" int h = 0;")
L.append(" for (int i = 0; i < N_CLASSES; i++) {")
L.append(" for (int j = i + 1; j < N_CLASSES; j++) {")
L.append(" double decision = SVM_INTERCEPT[h];")
L.append(" for (int f = 0; f < N_FEATURES; f++) {")
L.append(" decision += SVM_COEF[h][f] * norm[f];")
L.append(" }")
L.append(" if (decision > 0) scores[i] += 1.0;")
L.append(" else scores[j] += 1.0;")
L.append(" h++;")
L.append(" }")
L.append(" }")
L.append(" int best = 0;")
L.append(" for (int c = 1; c < N_CLASSES; c++) {")
L.append(" if (scores[c] > scores[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
# 3. LOGISTIC REGRESSION
elif model_name == "logreg" and hasattr(model, 'coef_'):
coefs = model.coef_
intercepts = model.intercept_
n_features = coefs.shape[1]
n_coef_classes = coefs.shape[0]
L.append(f"#define N_FEATURES {n_features}")
L.append(f"#define N_COEF_CLASSES {n_coef_classes}")
L.append("")
if quantize:
scale_factor = 127.0 / max(float(np.max(np.abs(coefs))), float(np.max(np.abs(intercepts))), 1e-6)
L.append(f"/* Quantization Scale: {scale_factor:.4f} */")
L.append(f"static const int8_t LOGREG_COEF[N_COEF_CLASSES][N_FEATURES] = {{")
for row in coefs:
vals = ", ".join([str(int(round(v * scale_factor))) for v in row])
L.append(f" {{{vals}}},")
L.append("};")
L.append("")
vals = ", ".join([str(int(round(v * scale_factor))) for v in intercepts])
L.append(f"static const int8_t LOGREG_INTERCEPT[N_COEF_CLASSES] = {{{vals}}};")
L.append("")
L.append("int predict(const float *features) {")
L.append(" int8_t q_features[6];")
L.append(" preprocess_vitals_int8(features, q_features);")
L.append(" int32_t scores[N_COEF_CLASSES];")
L.append(" for (int c = 0; c < N_COEF_CLASSES; c++) {")
L.append(f" scores[c] = (int32_t)LOGREG_INTERCEPT[c] * {int(scale_factor)};")
L.append(" for (int f = 0; f < N_FEATURES; f++) {")
L.append(" scores[c] += (int32_t)LOGREG_COEF[c][f] * q_features[f];")
L.append(" }")
L.append(" }")
if n_coef_classes == 1:
L.append(" return (scores[0] > 0) ? 1 : 0;")
else:
L.append(" int best = 0;")
L.append(" for (int c = 1; c < N_COEF_CLASSES; c++) {")
L.append(" if (scores[c] > scores[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
else:
L.append(f"static const double LOGREG_COEF[N_COEF_CLASSES][N_FEATURES] = {{")
for row in coefs:
vals = ", ".join([f"{v:.6f}" for v in row])
L.append(f" {{{vals}}},")
L.append("};")
L.append("")
vals = ", ".join([f"{v:.6f}" for v in intercepts])
L.append(f"static const double LOGREG_INTERCEPT[N_COEF_CLASSES] = {{{vals}}};")
L.append("")
L.append("int predict(const float *features) {")
L.append(" double norm[6];")
L.append(" preprocess_vitals(features, norm);")
L.append(" double scores[N_COEF_CLASSES];")
L.append(" for (int c = 0; c < N_COEF_CLASSES; c++) {")
L.append(" scores[c] = LOGREG_INTERCEPT[c];")
L.append(" for (int f = 0; f < N_FEATURES; f++) {")
L.append(" scores[c] += LOGREG_COEF[c][f] * norm[f];")
L.append(" }")
L.append(" }")
if n_coef_classes == 1:
L.append(" return (scores[0] > 0.0) ? 1 : 0;")
else:
L.append(" int best = 0;")
L.append(" for (int c = 1; c < N_COEF_CLASSES; c++) {")
L.append(" if (scores[c] > scores[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
# 4. SMALL NEURAL NETWORK (MLP)
elif model_name == "small_nn" and hasattr(model, 'coefs_'):
layers = model.coefs_
biases = model.intercepts_
arch = " -> ".join([str(l.shape[0]) for l in layers] + [str(layers[-1].shape[1])])
L.append(f"/* MLP Neural Network: {len(layers)} layers ({arch}) */")
L.append("")
if quantize:
max_val = max([float(np.max(np.abs(w))) for w in layers] + [float(np.max(np.abs(b))) for b in biases] + [1e-6])
scale_factor = 127.0 / max_val
L.append(f"/* INT8 Quantization Scale Factor: {scale_factor:.4f} */")
for idx, (W, b) in enumerate(zip(layers, biases)):
n_in, n_out = W.shape
L.append(f"static const int8_t W{idx}[{n_in}][{n_out}] = {{")
for row in W:
vals = ", ".join([str(int(round(v * scale_factor))) for v in row])
L.append(f" {{{vals}}},")
L.append("};")
bvals = ", ".join([str(int(round(v * scale_factor))) for v in b])
L.append(f"static const int8_t B{idx}[{n_out}] = {{{bvals}}};")
L.append("")
L.append("static inline int32_t relu_int(int32_t x) { return x > 0 ? x : 0; }")
L.append("")
L.append("int predict(const float *features) {")
L.append(" int8_t input[6];")
L.append(" preprocess_vitals_int8(features, input);")
for idx in range(len(layers)):
n_in = layers[idx].shape[0]
n_out = layers[idx].shape[1]
is_last = (idx == len(layers) - 1)
src = "input" if idx == 0 else f"a{idx-1}"
L.append(f" int32_t a{idx}[{n_out}];")
L.append(f" for (int j = 0; j < {n_out}; j++) {{")
L.append(f" a{idx}[j] = (int32_t)B{idx}[j] * {int(scale_factor)};")
L.append(f" for (int i = 0; i < {n_in}; i++) {{")
L.append(f" a{idx}[j] += (int32_t){src}[i] * W{idx}[i][j];")
L.append(" }")
if not is_last:
L.append(f" a{idx}[j] = relu_int(a{idx}[j]) / {int(scale_factor)};")
L.append(" }")
last_idx = len(layers) - 1
last_out = layers[-1].shape[1]
L.append(" int best = 0;")
L.append(f" for (int c = 1; c < {last_out}; c++) {{")
L.append(f" if (a{last_idx}[c] > a{last_idx}[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
else:
for idx, (W, b) in enumerate(zip(layers, biases)):
n_in, n_out = W.shape
L.append(f"#define L{idx}_IN {n_in}")
L.append(f"#define L{idx}_OUT {n_out}")
L.append(f"static const double W{idx}[{n_in}][{n_out}] = {{")
for row in W:
vals = ", ".join([f"{v:.6f}" for v in row])
L.append(f" {{{vals}}},")
L.append("};")
bvals = ", ".join([f"{v:.6f}" for v in b])
L.append(f"static const double B{idx}[{n_out}] = {{{bvals}}};")
L.append("")
L.append("static inline double relu_fp(double x) { return x > 0 ? x : 0; }")
L.append("")
L.append("int predict(const float *features) {")
L.append(" double input[6];")
L.append(" preprocess_vitals(features, input);")
for idx in range(len(layers)):
n_in = layers[idx].shape[0]
n_out = layers[idx].shape[1]
is_last = (idx == len(layers) - 1)
src = "input" if idx == 0 else f"a{idx-1}"
L.append(f" double a{idx}[{n_out}];")
L.append(f" for (int j = 0; j < {n_out}; j++) {{")
L.append(f" a{idx}[j] = B{idx}[j];")
L.append(f" for (int i = 0; i < {n_in}; i++) {{")
L.append(f" a{idx}[j] += {src}[i] * W{idx}[i][j];")
L.append(" }")
if not is_last:
L.append(f" a{idx}[j] = relu_fp(a{idx}[j]);")
L.append(" }")
last_idx = len(layers) - 1
last_out = layers[-1].shape[1]
L.append(" int best = 0;")
L.append(f" for (int c = 1; c < {last_out}; c++) {{")
L.append(f" if (a{last_idx}[c] > a{last_idx}[best]) best = c;")
L.append(" }")
L.append(" return best;")
L.append("}")
# 5. K-NEAREST NEIGHBORS (KNN)
elif model_name == "knn" and hasattr(model, '_fit_X'):
n_samples = min(model._fit_X.shape[0], 80)
n_feats = model._fit_X.shape[1]
L.append(f"#define N_NEIGHBORS {model.n_neighbors}")
L.append(f"#define N_SAMPLES {n_samples}")
L.append(f"#define N_FEATURES {n_feats}")
L.append("")
L.append("static const double REF[N_SAMPLES][N_FEATURES] = {")
for row in model._fit_X[:n_samples]:
vals = ", ".join([f"{v:.4f}" for v in row])
L.append(f" {{{vals}}},")
L.append("};")
L.append("")
labels_str = ", ".join([str(int(l)) for l in model._y[:n_samples]])
L.append(f"static const int LABELS[N_SAMPLES] = {{{labels_str}}};")
L.append("")
L.append("int predict(const float *features) {")
L.append(" double norm[6];")
L.append(" preprocess_vitals(features, norm);")
L.append(" double dists[N_SAMPLES];")
L.append(" for (int i = 0; i < N_SAMPLES; i++) {")
L.append(" dists[i] = 0.0;")
L.append(" for (int f = 0; f < N_FEATURES; f++) {")
L.append(" double d = norm[f] - REF[i][f];")
L.append(" dists[i] += d * d;")
L.append(" }")
L.append(" }")
L.append(" int votes[N_CLASSES] = {0};")
L.append(" for (int k = 0; k < N_NEIGHBORS; k++) {")
L.append(" int mi = 0;")
L.append(" for (int i = 1; i < N_SAMPLES; i++) {")
L.append(" if (dists[i] < dists[mi]) mi = i;")
L.append(" }")
L.append(" if (LABELS[mi] < N_CLASSES) votes[LABELS[mi]]++;")
L.append(" dists[mi] = 1e18;")
L.append(" }")
L.append(" int best = 0;")
L.append(" for (int i = 1; i < N_CLASSES; i++) {")
L.append(" if (votes[i] > votes[best]) best = i;")
L.append(" }")
L.append(" return best;")
L.append("}")
else:
return {"error": f"Model {model_name} cannot be exported to C."}
L.append("")
L.append("#endif /* HEARTFLOW_MODEL_H */")
L.append("")
return {"code": "\n".join(L)}
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