From a3a4bd92385314b0d9de79b26c0fa9b085901c0e Mon Sep 17 00:00:00 2001
From: Israel Figueroa <israel.figueroa@usm.cl>
Date: Sun, 16 Mar 2025 04:32:15 -0300
Subject: [PATCH] another

---
 LoaderClass.py | 53 +++++++++++++++++++++++++-------------------------
 1 file changed, 27 insertions(+), 26 deletions(-)

diff --git a/LoaderClass.py b/LoaderClass.py
index 823190b..996bce4 100644
--- a/LoaderClass.py
+++ b/LoaderClass.py
@@ -306,6 +306,33 @@ class GasSensorDataLoader:
 
         return self.dataset[ws]
 
+
+    def create_conv1d_dataset(self, measurament, r, l, window):
+
+      X_data = self.scaled_data[measurament]['data'].iloc[r:l]
+      Y_value = np.array([[self.scaled_data[measurament]['label'][key] for key in self.target_list]])
+      G_value = self.scaled_data[measurament]['sampleId']
+
+      total_samples = X_data.shape[0] - window + 1
+      sample_size = self.min_sample - window + 1
+
+      if sample_size > total_samples:
+        self.logger.warn(f"sample_size ({sample_size}) exceeds available samples ({total_samples}). Using available samples.")
+        sample_size = total_samples
+
+      random_indices = np.random.choice(total_samples, size=sample_size, replace=False)
+      g_sample = np.zeros((sample_size, 1))
+      y_sample = np.zeros((sample_size, self.target_len))
+      x_samples = np.zeros((sample_size, window, self.data_channels))
+
+
+      for i, index in enumerate(random_indices):
+        x_samples[i] = X_data.iloc[index:index + window].values
+        y_sample[i] = Y_value
+        g_sample[i] = G_value
+        
+      return x_samples, y_sample, g_sample
+
     def load_dataset_xboost(self):
 
         self.logger.info(f"Requested sample with threshold {self.threshold} for xboost")
@@ -381,32 +408,6 @@ class GasSensorDataLoader:
 
       return x_samples, y_sample, g_sample
 
-    def create_conv1d_dataset(self, measurament, r, l, window):
-
-      X_data = self.scaled_data[measurament]['data'].iloc[r:l]
-      Y_value = np.array([[self.scaled_data[measurament]['label'][key] for key in self.target_list]])
-      G_value = self.scaled_data[measurament]['sampleId']
-
-      total_samples = X_data.shape[0] - window + 1
-      sample_size = self.min_sample - window + 1
-
-      if sample_size > total_samples:
-        self.logger.warn(f"sample_size ({sample_size}) exceeds available samples ({total_samples}). Using available samples.")
-        sample_size = total_samples
-
-      random_indices = np.random.choice(total_samples, size=sample_size, replace=False)
-      g_sample = np.zeros((sample_size, 1))
-      y_sample = np.zeros((sample_size, self.target_len))
-      x_samples = np.zeros((sample_size, window, self.data_channels))
-
-
-      for i, index in enumerate(random_indices):
-        x_samples[i] = X_data.iloc[index:index + window].values
-        y_sample[i] = Y_value
-        g_sample[i] = G_value
-
-
-      return x_samples, y_sample, g_sample
 
     def stats(self):
         channel_columns = {}