add RVC

Author: agrimagsrl

MANIFEST

@@ -15,3 +15,5 @@ micromlgen/templates/kernel_function.jinja
 micromlgen/templates/pca_function.jinja
 micromlgen/templates/self_test.jinja
 micromlgen/templates/svm.jinja
+micromlgen/templates/pca/_dot_product.jinja
+micromlgen/templates/pca/pca.jinja

dist/micromlgen-0.11.tar.gz

@@ -1 +1 @@
-from micromlgen.micromlgen import port, port_pca
+from micromlgen.micromlgen import port, port_pca, port_rvm

dist/micromlgen-0.12.tar.gz

@@ -24,6 +24,26 @@ def port_pca(pca):
     })
 
 
+def port_rvm(clf, classmap=None, test_set=None, **kwargs):
+    from skbayes.rvm_ard_models import RVC
+    assert isinstance(clf, RVC), 'Not an RVC classifier'
+    return jinja('rvm/rvm.jinja', {
+        'clf': clf,
+        'FEATURES_DIM': clf.relevant_vectors_[0].shape[1],
+        'KERNEL_TYPE': clf.kernel,
+        'KERNEL_GAMMA': clf.gamma,
+        'KERNEL_COEF': clf.coef0,
+        'KERNEL_DEGREE': clf.degree,
+        'N_CLASSES': len(clf.classes_),
+        'classmap': classmap,
+        'X': test_set[0] if test_set else None,
+        'y': test_set[1] if test_set else None,
+        'enumerate': enumerate,
+        'zip': zip,
+        'round': round
+    })
+
+
 def port(clf,
          test_set=None,
          classmap=None,
@@ -56,3 +76,4 @@ def port(clf,
         'isAttiny': platform == 'attiny',
     }
     return jinja('svm.jinja', template_data)
+

micromlgen/__init__.py

@@ -4,18 +4,16 @@
  * Apply PCA to x vector.
  * Overrides the original vector.
  */
-void pca(float x[{{ X_DIM }}]) {
-    float u[{{ PCA_DIM }}];
+void pca(double x[{{ X_DIM }}]) {
+    double u[{{ PCA_DIM }}] = { 0 };
 
-    {% for vector in pca_components %}
-    u[{{ loop.index - 1 }}] = dot_product(x, {{ X_DIM }}, {% for vi in vector %} {% if loop.index > 1 %}, {% endif %} {{ F.round(vi, 9) }} {% endfor %});
+    {% for i, vector in F.enumerate(pca_components) %}
+        {% for chunk in F.chunk(vector, 100) %}
+            u[{{ i }}] += dot_product(x, {{ chunk|length }}, {% for vi in chunk %} {% if loop.index > 1 %}, {% endif %} {{ F.round(vi, 9) }} {% endfor %});
+        {% endfor %}
     {% endfor %}
 
     {% for vector in pca_components %}
     x[{{ loop.index - 1 }}] = u[{{ loop.index - 1 }}];
     {% endfor %}
-
-    {% for i in range(PCA_DIM, X_DIM) %}
-    x[{{ i }}] = 0;
-    {% endfor %}
 }

micromlgen/__pycache__/__init__.cpython-36.pyc

@@ -0,0 +1,27 @@
+{% include 'kernel_function.jinja' %}
+
+uint8_t predict(double *x) {
+    double decision[{{ clf.relevant_vectors_|length }}] = { 0 };
+    {% for i, (rv, cf, act, b) in enumerate(zip(clf.relevant_vectors_, clf.coef_, clf.active_, clf.intercept_)) %}
+        {% if rv.shape[0] == 0 %}
+            decision[{{ i }}] = {{ b }};
+        {% else %}
+            decision[{{ i }}] = (compute_kernel(x, {% for vi in rv[0] %}{% if loop.index > 1 %},{% endif %} {{ round(vi, 7) }}{% endfor %}) - {{ clf._x_mean[act][0] }} ) * {{ cf[act][0] / clf._x_std[act][0] }} + {{ b }};
+        {% endif %}
+    {% endfor %}
+
+    uint8_t idx = 0;
+    double val = decision[0];
+
+    for (uint8_t i = 1; i < {{ clf.relevant_vectors_|length }}; i++) {
+        if (decision[i] > val) {
+            idx = i;
+            val = decision[i];
+        }
+    }
+
+    return idx;
+}
+
+{% include 'self_test.jinja' %}
+{% include 'classmap.jinja' %}

micromlgen/__pycache__/micromlgen.cpython-36.pyc

@@ -11,7 +11,7 @@ void self_test() {
        {% endfor %}
     };
 
-    int y[{{ X|length }}] = { {% for yi in y %}{% if loop.index > 1 %},{% endif %} {{ yi }} {% endfor %} };
+    int y[{{ X|length }}] = { {% for yi in y %}{% if loop.index > 1 %},{% endif %} {{ yi|int }} {% endfor %} };
 
     for (int i = 0; i < {{ X|length }}; i++) {
         int predicted = predict(X[i]);

micromlgen/micromlgen.py

@@ -2,13 +2,13 @@
 setup(
   name = 'micromlgen',
   packages = ['micromlgen'],
-  version = '0.12',
+  version = '0.13',
   license='MIT',
   description = 'Generate C code for microcontrollers from Python\'s sklearn classifiers',
   author = 'Simone Salerno',
   author_email = 'eloquentarduino@gmail.com',
   url = 'https://github.com/eloquentarduino/micromlgen',
-  download_url = 'https://github.com/eloquentarduino/micromlgen/archive/v_012.tar.gz',
+  download_url = 'https://github.com/eloquentarduino/micromlgen/archive/v_013.tar.gz',
   keywords = ['ML', 'microcontrollers', 'sklearn', 'machine learning'],
   install_requires=[
     'jinja2',