LogLikelihood function, Newton optimizer: dtype passed (#242)

Author: gyrdym

CHANGELOG.md

@@ -1,5 +1,12 @@
 # Changelog
 
+## 16.17.3
+- Log Likelihood Cost function:
+  - `dtype` passed
+- Newton optimizer:
+  - `dtype` passed
+- Removed `package:ml_linalg/linalg.dart` and `package:ml_algo/ml_algo.dart` imports
+
 ## 16.17.2
 - Code quality:
   - Strict options turned on

benchmark/decision_tree_classifier.dart

@@ -5,7 +5,8 @@ import 'dart:math';
 import 'package:benchmark_harness/benchmark_harness.dart';
 import 'package:ml_algo/ml_algo.dart';
 import 'package:ml_dataframe/ml_dataframe.dart';
-import 'package:ml_linalg/linalg.dart';
+import 'package:ml_linalg/matrix.dart';
+import 'package:ml_linalg/vector.dart';
 
 const observationsNum = 300;
 const columnsNum = 11;

benchmark/kd_tree/kd_tree_querying.dart

@@ -3,7 +3,8 @@ import 'package:benchmark_harness/benchmark_harness.dart';
 import 'package:ml_algo/src/retrieval/kd_tree/kd_tree.dart';
 import 'package:ml_algo/src/retrieval/kd_tree/kd_tree_impl.dart';
 import 'package:ml_dataframe/ml_dataframe.dart';
-import 'package:ml_linalg/linalg.dart';
+import 'package:ml_linalg/matrix.dart';
+import 'package:ml_linalg/vector.dart';
 
 final k = 10;
 

benchmark/random_binary_projection_searcher/searcher_querying.dart

@@ -3,7 +3,8 @@ import 'package:benchmark_harness/benchmark_harness.dart';
 import 'package:ml_algo/ml_algo.dart';
 import 'package:ml_algo/src/retrieval/random_binary_projection_searcher/random_binary_projection_searcher_impl.dart';
 import 'package:ml_dataframe/ml_dataframe.dart';
-import 'package:ml_linalg/linalg.dart';
+import 'package:ml_linalg/matrix.dart';
+import 'package:ml_linalg/vector.dart';
 
 final k = 10;
 final digitCapacity = 10;

lib/src/classifier/_helpers/create_log_likelihood_optimizer.dart

@@ -51,6 +51,7 @@ LinearOptimizer createLogLikelihoodOptimizer(
     linkFunction: linkFunction,
     positiveLabel: positiveLabel,
     negativeLabel: negativeLabel,
+    dtype: dtype,
   );
   final normalizedLabels =
       normalizeClassLabels(labels, positiveLabel, negativeLabel);

lib/src/cost_function/cost_function_factory.dart

@@ -1,12 +1,14 @@
 import 'package:ml_algo/src/cost_function/cost_function.dart';
 import 'package:ml_algo/src/cost_function/cost_function_type.dart';
 import 'package:ml_algo/src/link_function/link_function.dart';
+import 'package:ml_linalg/dtype.dart';
 
 abstract class CostFunctionFactory {
   CostFunction createByType(
     CostFunctionType type, {
     LinkFunction? linkFunction,
     num? positiveLabel,
     num? negativeLabel,
+    DType dtype,
   });
 }

lib/src/cost_function/cost_function_factory_impl.dart

@@ -4,6 +4,7 @@ import 'package:ml_algo/src/cost_function/cost_function_type.dart';
 import 'package:ml_algo/src/cost_function/least_square_cost_function.dart';
 import 'package:ml_algo/src/cost_function/log_likelihood_cost_function.dart';
 import 'package:ml_algo/src/link_function/link_function.dart';
+import 'package:ml_linalg/dtype.dart';
 
 class CostFunctionFactoryImpl implements CostFunctionFactory {
   const CostFunctionFactoryImpl();
@@ -14,6 +15,7 @@ class CostFunctionFactoryImpl implements CostFunctionFactory {
     LinkFunction? linkFunction,
     num? positiveLabel,
     num? negativeLabel,
+    DType dtype = DType.float32,
   }) {
     switch (type) {
       case CostFunctionType.logLikelihood:
@@ -34,6 +36,7 @@ class CostFunctionFactoryImpl implements CostFunctionFactory {
           linkFunction,
           positiveLabel,
           negativeLabel,
+          dtype,
         );
 
       case CostFunctionType.leastSquare:

lib/src/cost_function/least_square_cost_function.dart

@@ -1,5 +1,5 @@
 import 'package:ml_algo/src/cost_function/cost_function.dart';
-import 'package:ml_linalg/linalg.dart';
+import 'package:ml_linalg/matrix.dart';
 
 class LeastSquareCostFunction implements CostFunction {
   const LeastSquareCostFunction();

lib/src/cost_function/log_likelihood_cost_function.dart

@@ -2,17 +2,20 @@ import 'package:ml_algo/src/cost_function/cost_function.dart';
 import 'package:ml_algo/src/helpers/normalize_class_labels.dart';
 import 'package:ml_algo/src/helpers/validate_class_labels.dart';
 import 'package:ml_algo/src/link_function/link_function.dart';
-import 'package:ml_linalg/linalg.dart';
+import 'package:ml_linalg/dtype.dart';
+import 'package:ml_linalg/matrix.dart';
+import 'package:ml_linalg/vector.dart';
 
 class LogLikelihoodCostFunction implements CostFunction {
-  LogLikelihoodCostFunction(
-      this._linkFunction, this._positiveLabel, this._negativeLabel) {
+  LogLikelihoodCostFunction(this._linkFunction, this._positiveLabel,
+      this._negativeLabel, this._dtype) {
     validateClassLabels(_positiveLabel, _negativeLabel);
   }
 
   final LinkFunction _linkFunction;
   final num _positiveLabel;
   final num _negativeLabel;
+  final DType _dtype;
 
   @override
   double getCost(Matrix x, Matrix w, Matrix y) {
@@ -39,9 +42,9 @@ class LogLikelihoodCostFunction implements CostFunction {
   @override
   Matrix getHessian(Matrix x, Matrix w, Matrix y) {
     final prediction = _linkFunction.link(x * w).toVector();
-    final ones = Vector.filled(x.rowsNum, 1.0, dtype: x.dtype);
+    final ones = Vector.filled(x.rowsNum, 1.0, dtype: _dtype);
     final V = Matrix.diagonal((prediction * (ones - prediction)).toList(),
-        dtype: x.dtype);
+        dtype: _dtype);
 
     return x.transpose() * V * x;
   }

lib/src/linear_optimizer/initial_coefficients_generator/initial_coefficients_generator.dart

@@ -1,4 +1,4 @@
-import 'package:ml_linalg/linalg.dart';
+import 'package:ml_linalg/vector.dart';
 
 abstract class InitialCoefficientsGenerator {
   Vector generate(int length);