emlearn · jonnor · Apr 19, 2026 · Apr 19, 2026 · Apr 19, 2026 · Apr 19, 2026
diff --git a/Makefile b/Makefile
@@ -83,7 +83,7 @@ webassembly: $(WEBASSEMBLY_MICROPYTHON)
 
 
 check_unix: $(UNIX_MICROPYTHON)
-	$(UNIX_MICROPYTHON) tests/test_all.py test_iir,test_fft,test_arrayutils
+	$(UNIX_MICROPYTHON) tests/test_all.py test_iir,test_fft,test_arrayutils,test_linreg
 	# TODO: enable more modules
 
 rp2: $(PORT_DIR)

diff --git a/docs/getting_started_browser.rst b/docs/getting_started_browser.rst
@@ -0,0 +1,78 @@
+
+.. Places parent toc into the sidebar
+
+:parenttoc: True
+
+.. _getting_started_browser:
+
+=========================
+Getting started for browser
+=========================
+
+.. currentmodule:: emlearn-micropython
+
+emlearn-micropython runs on most platforms that MicroPython does.
+This includes running in a web browser, using the `Webassembly port <https://github.com/micropython/micropython/tree/master/ports/webassembly>`_ of MicroPython.
+The browser integration is enabled by `PyScript <https://docs.pyscript.net/>`_.
+
+Prerequisites
+===========================
+
+A web browser and a file editor.
+An CPython 3.10+ installation is also recommended, to act as web server.
+
+
+emlearn-micropython build for browser
+==================================
+
+We publish a pre-built MicroPython for each release.
+This includes emlearn-micropython as external C modules.
+
+There are two files needed, the `micropython.mjs` and `micropython.wasm`.
+They can be downloaded from:
+
+- https://raw.githubusercontent.com/emlearn/emlearn-micropython/refs/heads/gh-pages/builds/latest/ports/webassembly/micropython.mjs
+- https://raw.githubusercontent.com/emlearn/emlearn-micropython/refs/heads/gh-pages/builds/latest/ports/webassembly/micropython.wasm
+
+The ``latest`` version can be changed to a tag to have a specific version (``0.11.0`` or later).
+
+
+Setup web page
+==================================
+
+Create an `index.html` page with the following contents:
+
+.. literalinclude:: helloworld_browser/index.html
+   :language: html
+
+Make sure that you have ``micropython.mjs`` and ``micropython.wasm`` in the same directory.
+
+Try it out 
+========================
+
+Start a HTTP server to serve the files
+
+.. code-block:: console
+
+    python -m http.server
+
+Open your browser at http://localhost:8000
+
+The MicroPython code using ``emlearn_linreg`` from emlearn-micropython should automatically run when you load the page.
+The webpage should show an output like like:
+
+``Input: [10.0, 75.0], prediction: 16.96 C``
+
+
+Serving from device
+====================================
+
+On a MicroPython device with networking (like ESP32),
+it can serve the browser frontend to clients.
+
+We recommend using the excellent `MicroDot web framework <https://microdot.readthedocs.io/en/latest/>`_.
+
+To be offline compatible, download PyScript and the MicroPython build files to your PC, and copy it to the device. Then update the HTML to have the local paths. See `PyScript offline <https://docs.pyscript.net/2026.3.1/user-guide/offline/#getting-micropython>`_ documentation for more information.
+
+
+
diff --git a/docs/helloworld_browser/index.html b/docs/helloworld_browser/index.html
@@ -0,0 +1,47 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+      <meta charset="utf-8" />
+      <meta name="viewport" content="width=device-width,initial-scale=1" />
+      <title>emlearn-micropython in browser with PyScript</title>
+      <link rel="stylesheet" href="https://pyscript.net/releases/2026.3.1/core.css">
+      <script type="module" src="https://pyscript.net/releases/2026.3.1/core.js"></script>
+</head>
+<body>
+  <!-- This should be in a config file, using mpy-config for brevity. -->
+  <mpy-config>
+    interpreter = "/micropython.mjs"
+  </mpy-config>
+  <script type="mpy">
+    from pyscript import document
+
+    import emlearn_linreg
+    import array
+
+    # Predict temperature from (hour_of_day, humidity)
+    # y = 0.5*hour - 0.1*humidity + 15  + noise
+    X = array.array('f', [
+         8, 80,   # 8am, 80% humidity
+        12, 60,   # noon
+        16, 55,   # 4pm
+        20, 70,   # 8pm
+         0, 85,   # midnight
+    ])
+    y = array.array('f', [15.2, 18.4, 20.1, 16.8, 13.5])
+
+    model = emlearn_linreg.new(2, 0.01, 0.5, 0.0001)
+    emlearn_linreg.train(model, X, y, max_iterations=100, tolerance=1e-6)
+
+    mse = model.score_mse(X, y)
+    print(f"MSE: {mse:.4f}")
+
+    new_sample = array.array('f', [10, 75])  # 10am, 75% humidity
+    out = model.predict(new_sample)
+    print(f"Predicted temperature: {out:.1f} C")
+
+    document.body.append(f"Input: {list(new_sample)}, prediction: {out:.2f} C")
+
+
+  </script>
+</body>
+</html>
diff --git a/docs/user_guide.rst b/docs/user_guide.rst
@@ -16,6 +16,7 @@ User Guide
 
    getting_started_host.rst
    getting_started_device.rst
+   getting_started_browser.rst
    support.rst
    native_modules.rst
    external_modules.rst

diff --git a/examples/datasets/california/X_test.npy b/examples/datasets/california/X_test.npy
diff --git a/examples/datasets/california/X_train.npy b/examples/datasets/california/X_train.npy
diff --git a/examples/datasets/california/prepare.py b/examples/datasets/california/prepare.py
@@ -0,0 +1,175 @@
+
+#!/usr/bin/env python3
+"""
+Download and preprocess California housing dataset for MicroPython testing.
+Saves scaled train/test splits as .npy files.
+"""
+
+import os
+import time
+
+import numpy as np
+from sklearn.linear_model import ElasticNet
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+
+def prepare_california_housing_data(data_dir, sample=None):
+    """Download, preprocess and save California housing dataset."""
+
+    print("Downloading California housing dataset...")
+    # Load the dataset
+    housing = fetch_california_housing()
+    X, y = housing.data, housing.target
+
+    if sample is not None:
+        indices = np.random.choice(X.shape[0], size=sample, replace=False)
+        X = X[indices]
+        y = y[indices]
+
+    print(f"Dataset shape: X={X.shape}, y={y.shape}")
+    print(f"Features: {housing.feature_names}")
+    print(f"Target: median house value in hundreds of thousands of dollars")
+
+    # Split into train/test (80/20)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42
+    )
+
+    print(f"Train set: X={X_train.shape}, y={y_train.shape}")
+    print(f"Test set: X={X_test.shape}, y={y_test.shape}")
+
+    # Scale the features (standardization)
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+
+    print("\nScaling applied:")
+    print(f"Feature means: {scaler.mean_}")
+    print(f"Feature stds: {scaler.scale_}")
+
+    # Convert to float32 for MicroPython compatibility
+    X_train_scaled = X_train_scaled.astype(np.float32)
+    X_test_scaled = X_test_scaled.astype(np.float32)
+    y_train = y_train.astype(np.float32)
+    y_test = y_test.astype(np.float32)
+
+    # Save as .npy files
+    np.save(os.path.join(data_dir, 'X_train.npy'), X_train_scaled)
+    np.save(os.path.join(data_dir, 'X_test.npy'), X_test_scaled)
+    np.save(os.path.join(data_dir, 'y_train.npy'), y_train)
+    np.save(os.path.join(data_dir, 'y_test.npy'), y_test)
+
+    print("\nSaved files:")
+    print(f"X_train.npy: {X_train_scaled.shape} float32")
+    print(f"X_test.npy: {X_test_scaled.shape} float32")
+    print(f"y_train.npy: {y_train.shape} float32")
+    print(f"y_test.npy: {y_test.shape} float32")
+
+    # Print some statistics for verification
+    print("\nData statistics:")
+    print(f"X_train range: [{X_train_scaled.min():.3f}, {X_train_scaled.max():.3f}]")
+    print(f"y_train range: [{y_train.min():.3f}, {y_train.max():.3f}]")
+    print(f"y_train mean: {y_train.mean():.3f}")
+
+    return X_train_scaled, X_test_scaled, y_train, y_test
+
+
+
+def load_data(data_dir):
+    """Load the preprocessed California housing data."""
+    print("Loading data...")
+    X_train = np.load(os.path.join(data_dir, 'X_train.npy'))
+    X_test = np.load(os.path.join(data_dir, 'X_test.npy'))
+    y_train = np.load(os.path.join(data_dir, 'y_train.npy'))
+    y_test = np.load(os.path.join(data_dir, 'y_test.npy'))
+
+    print(f"Train set: X={X_train.shape}, y={y_train.shape}")
+    print(f"Test set: X={X_test.shape}, y={y_test.shape}")
+    print(f"Data types: X={X_train.dtype}, y={y_train.dtype}")
+
+    return X_train, X_test, y_train, y_test
+
+def test_elasticnet_configurations(data_dir):
+    """Test different ElasticNet configurations to find good baselines."""
+
+    X_train, X_test, y_train, y_test = load_data(data_dir)
+
+    # Test configurations: (alpha, l1_ratio, description)
+    configs = [
+        (0.0, 0.0, "No regularization (OLS)"),
+        (0.01, 0.0, "Ridge (alpha=0.01)"),
+        (0.01, 1.0, "LASSO (alpha=0.01)"),
+        (0.01, 0.5, "ElasticNet (alpha=0.01, l1_ratio=0.5)"),
+        (0.001, 0.5, "ElasticNet (alpha=0.001, l1_ratio=0.5)"),
+        (0.1, 0.5, "ElasticNet (alpha=0.1, l1_ratio=0.5)"),
+    ]
+
+    print("\n" + "="*70)
+    print("ElasticNet Configuration Comparison")
+    print("="*70)
+    print(f"{'Configuration':<35} {'Train MSE':<12} {'Test MSE':<12} {'R²':<8} {'Time':<8}")
+    print("-"*70)
+
+    results = []
+
+    for alpha, l1_ratio, description in configs:
+        start_time = time.time()
+
+        # Create and train model
+        if alpha == 0.0:
+            # Use regular linear regression for no regularization
+            from sklearn.linear_model import LinearRegression
+            model = LinearRegression()
+        else:
+            model = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, max_iter=2000, random_state=42)
+
+        model.fit(X_train, y_train)
+
+        # Make predictions
+        y_train_pred = model.predict(X_train)
+        y_test_pred = model.predict(X_test)
+
+        # Calculate metrics
+        train_mse = mean_squared_error(y_train, y_train_pred)
+        test_mse = mean_squared_error(y_test, y_test_pred)
+        test_r2 = r2_score(y_test, y_test_pred)
+
+        elapsed_time = time.time() - start_time
+
+        print(f"{description:<35} {train_mse:<12.6f} {test_mse:<12.6f} {test_r2:<8.3f} {elapsed_time:<8.3f}")
+
+        results.append({
+            'config': description,
+            'alpha': alpha,
+            'l1_ratio': l1_ratio,
+            'train_mse': train_mse,
+            'test_mse': test_mse,
+            'r2': test_r2,
+            'time': elapsed_time,
+            'model': model
+        })
+
+    return results
+
+
+
+def main():
+
+    here = os.path.dirname(__file__)
+    data_dir = here
+
+    # Prepare the data
+    prepare_california_housing_data(data_dir, sample=4000)
+
+    # Test different configurations
+    results = test_elasticnet_configurations(data_dir)
+
+    #print(results)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/examples/datasets/california/y_test.npy b/examples/datasets/california/y_test.npy
diff --git a/examples/datasets/california/y_train.npy b/examples/datasets/california/y_train.npy