diff --git a/CVResults/FieldA/DualAQD/Responsivity_feature-0/Global_Ids_s-0.txt b/CVResults/FieldA/DualAQD/Responsivity_feature-0/Global_Ids_s-0.txt
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+Global Impact of feature 1 (slope) on responsivity of feature 0 = 23.1000 (+/- 11.5652)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 13.1800 (+/- 8.1875)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 60.6200 (+/- 9.0653)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 20.5600 (+/- 13.7095)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 63.9600 (+/- 12.3440)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 65.9600 (+/- 11.2871)
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+Overall MSE 154.111542 (+/- 4.543602)
+Overall PICP 0.947132 (+/- 0.007156)
+Overall MPIW 47.828667 (+/- 1.591790)
\ No newline at end of file
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diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3.txt b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3.txt
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--- /dev/null
+++ b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3.txt
@@ -0,0 +1,6 @@
+Global Impact of feature 1 (slope) on responsivity of feature 0 = 13.7600 (+/- 1.7929)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 9.4200 (+/- 1.5348)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 43.3800 (+/- 1.5217)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 12.9800 (+/- 1.7331)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 15.3200 (+/- 1.7394)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 14.0000 (+/- 1.5047)
diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3_fold-11.npy b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3_fold-11.npy
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diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6.txt b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6.txt
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+++ b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6.txt
@@ -0,0 +1,6 @@
+Global Impact of feature 1 (slope) on responsivity of feature 0 = 15.7000 (+/- 2.9655)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 18.3000 (+/- 3.4296)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 33.5000 (+/- 2.7148)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 0.6800 (+/- 0.2993)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 17.9600 (+/- 4.8578)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 19.6600 (+/- 3.0372)
diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6_fold-11.npy b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6_fold-11.npy
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diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.7.txt b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.7.txt
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+++ b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.7.txt
@@ -0,0 +1,6 @@
+Global Impact of feature 1 (slope) on responsivity of feature 0 = 13.7600 (+/- 1.7929)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 9.4200 (+/- 1.5348)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 43.3800 (+/- 1.5217)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 12.9800 (+/- 1.7331)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 15.3200 (+/- 1.7394)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 14.0000 (+/- 1.5047)
diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.7_fold-11.npy b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.7_fold-11.npy
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diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.8.txt b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.8.txt
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+++ b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.8.txt
@@ -0,0 +1,6 @@
+Global Impact of feature 1 (slope) on responsivity of feature 0 = 19.2000 (+/- 3.1228)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 18.8200 (+/- 4.2731)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 36.4600 (+/- 2.4365)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 1.1200 (+/- 0.5231)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 21.3400 (+/- 5.9066)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 20.7600 (+/- 3.0552)
diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.8_fold-11.npy b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.8_fold-11.npy
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diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-1.txt b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-1.txt
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--- /dev/null
+++ b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-1.txt
@@ -0,0 +1,6 @@
+Global Impact of feature 1 (slope) on responsivity of feature 0 = 24.6600 (+/- 3.4676)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 22.4200 (+/- 4.6891)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 41.3800 (+/- 2.9236)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 2.2400 (+/- 1.2387)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 25.4600 (+/- 6.4919)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 22.7400 (+/- 2.9611)
diff --git a/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-1_fold-11.npy b/CVResults/FieldA/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-1_fold-11.npy
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+Overall MSE 147.245937 (+/- 8.167470)
+Overall PICP 0.943636 (+/- 0.003799)
+Overall MPIW 47.058537 (+/- 1.423659)
\ No newline at end of file
diff --git a/CVResults/FieldA/MCDropout/temp/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3.txt b/CVResults/FieldA/MCDropout/temp/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3.txt
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+Global Impact of feature 1 (slope) on responsivity of feature 0 = 13.7600 (+/- 1.7929)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 9.4200 (+/- 1.5348)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 43.3800 (+/- 1.5217)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 12.9800 (+/- 1.7331)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 15.3200 (+/- 1.7394)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 14.0000 (+/- 1.5047)
diff --git a/CVResults/FieldA/MCDropout/temp/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3_fold-11.npy b/CVResults/FieldA/MCDropout/temp/Responsivity_feature-0/Global_Ids_s-0_epsi-0.3_fold-11.npy
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+Global Impact of feature 1 (slope) on responsivity of feature 0 = 13.7600 (+/- 1.7929)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 9.4200 (+/- 1.5348)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 43.3800 (+/- 1.5217)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 12.9800 (+/- 1.7331)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 15.3200 (+/- 1.7394)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 14.0000 (+/- 1.5047)
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+Overall MSE 148.580436 (+/- 7.658737)
+Overall PICP 0.942813 (+/- 0.003379)
+Overall MPIW 46.960595 (+/- 1.274586)
\ No newline at end of file
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diff --git a/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6.txt b/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6.txt
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+++ b/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6.txt
@@ -0,0 +1,6 @@
+Global Impact of feature 1 (slope) on responsivity of feature 0 = 13.0600 (+/- 4.9800)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 82.5400 (+/- 9.5574)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 1.7800 (+/- 1.4239)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 5.5000 (+/- 7.0275)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 8.2800 (+/- 5.3113)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 0.8000 (+/- 0.6387)
diff --git a/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6_fold-11.npy b/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6_fold-11.npy
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@@ -0,0 +1,6 @@
+Global Impact of feature 1 (slope) on responsivity of feature 0 = 17.0600 (+/- 6.8311)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 82.2000 (+/- 11.2467)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 3.2400 (+/- 1.8413)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 10.4600 (+/- 8.1580)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 12.4400 (+/- 6.0112)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 1.8000 (+/- 0.9033)
diff --git a/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.8_fold-11.npy b/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.8_fold-11.npy
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diff --git a/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-1.txt b/CVResults/FieldB/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-1.txt
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+Global Impact of feature 1 (slope) on responsivity of feature 0 = 22.4600 (+/- 8.5809)
+Global Impact of feature 2 (tpi) on responsivity of feature 0 = 81.9600 (+/- 10.4934)
+Global Impact of feature 3 (aspect_rad) on responsivity of feature 0 = 4.5800 (+/- 2.3383)
+Global Impact of feature 4 (prec_cy_g) on responsivity of feature 0 = 16.4400 (+/- 9.1362)
+Global Impact of feature 5 (vv_cy_f) on responsivity of feature 0 = 15.4800 (+/- 6.3801)
+Global Impact of feature 6 (vh_cy_f) on responsivity of feature 0 = 3.3800 (+/- 1.5911)
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+Overall MSE 50.021319 (+/- 4.292616)
+Overall PICP 0.947743 (+/- 0.003755)
+Overall MPIW 27.537514 (+/- 1.226215)
\ No newline at end of file
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diff --git a/CVResults/Synth/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.4.txt b/CVResults/Synth/MCDropout/Responsivity_feature-0/Global_Ids_s-0_epsi-0.4.txt
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+Global Impact of feature 1 (x2) on responsivity of feature 0 = 33.6600 (+/- 1.4968)
+Global Impact of feature 2 (x3) on responsivity of feature 0 = 76.9600 (+/- 1.4051)
+Global Impact of feature 3 (x4) on responsivity of feature 0 = 2.9200 (+/- 0.7167)
+Global Impact of feature 4 (x5) on responsivity of feature 0 = 0.4000 (+/- 0.1549)
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+Global Impact of feature 1 (x2) on responsivity of feature 0 = 43.7400 (+/- 1.4941)
+Global Impact of feature 2 (x3) on responsivity of feature 0 = 83.7600 (+/- 1.1271)
+Global Impact of feature 3 (x4) on responsivity of feature 0 = 9.7000 (+/- 1.0208)
+Global Impact of feature 4 (x5) on responsivity of feature 0 = 0.7400 (+/- 0.3470)
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+Global Impact of feature 1 (x2) on responsivity of feature 0 = 53.9000 (+/- 1.3950)
+Global Impact of feature 2 (x3) on responsivity of feature 0 = 89.4200 (+/- 1.0787)
+Global Impact of feature 3 (x4) on responsivity of feature 0 = 24.9000 (+/- 1.2625)
+Global Impact of feature 4 (x5) on responsivity of feature 0 = 1.3200 (+/- 0.3124)
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+Overall MSE 0.006784 (+/- 0.001324)
+Overall PICP 0.994444 (+/- 0.003370)
+Overall MPIW 0.624564 (+/- 0.015191)
+Overall DIFF nan (+/- nan)
\ No newline at end of file
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+Global Impact of feature 1 (x2) on responsivity of feature 0 = 30.1000 (+/- 1.4567)
+Global Impact of feature 2 (x3) on responsivity of feature 0 = 60.2800 (+/- 1.4372)
+Global Impact of feature 3 (x4) on responsivity of feature 0 = 14.2000 (+/- 1.3943)
+Global Impact of feature 4 (x5) on responsivity of feature 0 = 0.6200 (+/- 0.2441)
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+Global Impact of feature 1 (x2) on responsivity of feature 0 = 35.0200 (+/- 1.5911)
+Global Impact of feature 2 (x3) on responsivity of feature 0 = 70.2400 (+/- 2.2782)
+Global Impact of feature 3 (x4) on responsivity of feature 0 = 15.8600 (+/- 1.2265)
+Global Impact of feature 4 (x5) on responsivity of feature 0 = 0.8200 (+/- 0.3736)
diff --git a/CVResults/Synth/temp/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6_fold-11.npy b/CVResults/Synth/temp/Responsivity_feature-0/Global_Ids_s-0_epsi-0.6_fold-11.npy
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+Global Impact of feature 1 (x2) on responsivity of feature 0 = 45.3000 (+/- 1.4622)
+Global Impact of feature 2 (x3) on responsivity of feature 0 = 78.7600 (+/- 1.0151)
+Global Impact of feature 3 (x4) on responsivity of feature 0 = 28.4400 (+/- 2.9268)
+Global Impact of feature 4 (x5) on responsivity of feature 0 = 1.1800 (+/- 0.4936)
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diff --git a/DataLoader.py b/DataLoader.py
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index 0000000..451a734
--- /dev/null
+++ b/DataLoader.py
@@ -0,0 +1,85 @@
+import h5py
+import numpy as np
+# import matplotlib.pyplot as plt
+
+
+class DataLoader:
+
+    def __init__(self, name):
+        self.X, self.Y, self.types, self.names, self.modelType = np.zeros(0), np.zeros(0), None, None, None
+        if name == "Synth":
+            self.synthetic()
+            self.modelType = "NN"
+        elif name == "FieldA":
+            self.fieldA()
+            self.modelType = "Hyper3DNetLiteReg"
+        elif name == "FieldB":
+            self.fieldB()
+            self.modelType = "Hyper3DNetLiteReg"
+
+        # Shuffle dataset
+        indexes = np.arange(len(self.X))
+        np.random.seed(7)
+        np.random.shuffle(indexes)
+        self.X = self.X[indexes]
+        self.Y = self.Y[indexes]
+
+    def fieldA(self):
+        hdf5_file = h5py.File('Datasets//fieldA_dataset.hdf5', mode='r')
+        X = np.array(hdf5_file["data"][...]).astype(np.float32)
+        Y = np.array(hdf5_file["target"][...])
+        # Remove inputs where y==0
+        valid = [np.all(Y[ind, :, :] > 0) for ind in range(Y.shape[0])]
+        valid = np.where(valid)[0]
+        self.X = X[valid][:, :, [0, 1, 3, 4, 5, 6, 7], :, :]
+        self.Y = Y[valid][:, :, :]
+        self.names = ['aa_n', 'slope', 'tpi', 'aspect_rad', 'prec_cy_g', 'vv_cy_f', 'vh_cy_f']
+        self.types = ['real'] * self.X.shape[2]
+        # Code to save dataset
+        # import h5py
+        # hdf5_path = 'fieldA_dataset.hdf5'
+        # hdf5_file = h5py.File(hdf5_path, mode='w')
+        # hdf5_file.create_dataset("data", trainx.shape, np.float32)
+        # hdf5_file.create_dataset("target", train_y.shape, np.float32)
+        # hdf5_file["target"][:, ...] = train_y  # Save yield data
+        # hdf5_file["data"][:, ...] = trainx  # Save agg data
+        # hdf5_file.close()
+
+    def fieldB(self):
+        hdf5_file = h5py.File('Datasets//fieldB_dataset.hdf5', mode='r')
+        X = np.array(hdf5_file["data"][...]).astype(np.float32)
+        Y = np.array(hdf5_file["target"][...])
+        # Remove inputs where y==0
+        valid = [np.all(Y[ind, :, :] > 0) for ind in range(Y.shape[0])]
+        valid = np.where(valid)[0]
+        self.X = X[valid][:, :, [0, 1, 3, 4, 5, 6, 7], :, :]
+        self.Y = Y[valid][:, :, :]
+        self.names = ['aa_n', 'slope', 'tpi', 'aspect_rad', 'prec_cy_g', 'vv_cy_f', 'vh_cy_f']
+        self.types = ['real'] * self.X.shape[2]
+
+    def synthetic(self, n=10000):
+        def sigmoid(x):
+            return 1 / (1 + np.exp(-x))
+        np.random.seed(7)
+        # Define features
+        x1 = np.random.uniform(0, 1, size=n)  # x1 \in [0, 1]
+        x2 = np.random.uniform(-3, 3, size=n)  # x2 \in [1, 2]
+        x3 = np.random.uniform(1, 2, size=n)  # x3 \in [1, 2]
+        x4 = np.random.uniform(1, 2, size=n)  # x4 \in [1, 2]
+        x5 = np.random.uniform(0, 2, size=n)  # x5 \in [1, 2]
+        self.X = np.array([x1, x2, x3, x4, x5]).T
+        # Calculate output
+        self.Y = sigmoid((10 * x1 - 5) + x2) * (x3 ** 2) * x4 + 10 * x5
+        self.names = ['x1', 'x2', 'x3', 'x4', 'x5']
+        self.types = ['real'] * self.X.shape[1]
+        # Draw target response curves
+        # Y = np.zeros((n, 50))
+        # for i, x in enumerate(self.X):
+        #     xvector = np.repeat(np.reshape(x, (1, len(x))), 50, axis=0)
+        #     for j, xs in enumerate(np.linspace(start=0, stop=1, num=50)):
+        #         xvector[j, 0] = xs  # Replace the s-th value
+        #         Y[i, j] = sigmoid((10 * xvector[j, 0] - 5) + xvector[j, 1]) * (xvector[j, 2] ** 2) * xvector[j, 3] + \
+        #                   10 * xvector[j, 4]
+        # plt.figure()
+        # for p in Y:
+        #     plt.plot(p - p[0])
diff --git a/Datasets/fieldA_dataset.hdf5 b/Datasets/fieldA_dataset.hdf5
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diff --git a/Datasets/fieldB_dataset.hdf5 b/Datasets/fieldB_dataset.hdf5
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diff --git a/FDA/FDA.py b/FDA/FDA.py
new file mode 100644
index 0000000..0e1c378
--- /dev/null
+++ b/FDA/FDA.py
@@ -0,0 +1,128 @@
+#  Helper functions used for Functional Data Analysis
+# import umap
+import skfda
+import numpy as np
+from skfda.preprocessing.dim_reduction.feature_extraction import FPCA
+
+
+def function_alignment(Rcurves: np.ndarray):
+    """Align response curves according to the center of PIS
+    @param Rcurves: Array with shape (N, num, 3) (N resp. curves with a range of num values).
+                    Upper bound response curve: Rcurves[:, :, 0],
+                    Lower bound response curve: Rcurves[:, :, 1],
+                    Predicted response curve: Rcurves[:, :, 2].
+    """
+    # Calculate minimum value of each curve in ymean
+    mins = np.min(Rcurves, axis=1)  # Rcurves[:, 0]
+    mins = np.repeat(a=np.reshape(mins, (len(mins), 1)), repeats=Rcurves.shape[1], axis=1)
+    # Perform alignment subtracting mins
+    Rcurves[:, :] -= mins
+    return Rcurves
+
+
+def smooth_curves(Rcurves: np.ndarray) -> np.ndarray:
+    """Smooth curves using B-Spline basis representation"""
+    if Rcurves.ndim > 1:
+        num = Rcurves.shape[1]
+    else:
+        num = Rcurves.shape[0]
+    dataGrid = skfda.FDataGrid(data_matrix=Rcurves, grid_points=np.linspace(0, 1, num))
+    basis = skfda.representation.basis.BSpline(n_basis=11)
+    basis_fd = dataGrid.to_basis(basis)
+    return basis_fd.to_grid(grid_points=np.linspace(0, 1, num)).data_matrix[:, :, 0]
+
+
+def F_PCA(Rcurves: np.ndarray, n_components: int = 3):
+    """Functional-PCA"""
+    num = Rcurves.shape[1]
+    dataGrid = skfda.FDataGrid(data_matrix=Rcurves, grid_points=np.linspace(0, 1, num))
+    fpca = FPCA(n_components=n_components)
+    transformed = fpca.fit_transform(dataGrid)
+    # fpca.components_.plot()
+    # fig = plt.figure()
+    # ax = fig.add_subplot(projection='3d')
+    # color = cm.cool(np.linspace(0, 1, len(transformed)))
+    # ax.scatter(transformed[:, 0], transformed[:, 1], transformed[:, 2], alpha=.1)
+    # c1, c2 = 50, 600
+    # ax.scatter(transformed[c1, 0], transformed[c1, 1], transformed[c1, 2], s=51, c='r', alpha=1)
+    # ax.scatter(transformed[c2, 0], transformed[c2, 1], transformed[c2, 2], s=51, c='r', alpha=1)
+    # plt.figure()
+    # plt.plot(Rcurves[50, :])
+    # plt.plot(Rcurves[600, :])
+    # # Plot using UMAP
+    # reducer = umap.UMAP(metric='precomputed', n_components=1, n_neighbors=30, n_epochs=1000)
+    # distance_matrix = pairwise_distance(Rcurves)
+    # new_order = reducer.fit_transform(distance_matrix)
+    # # Sort curves according to the new order
+    # indices = np.argsort(new_order[:, 0])
+    # plt.figure()
+    # color = cm.Cool(np.linspace(0, 1, len(Rcurves)))
+    # for c, g in Rcurves:
+    #     plt.plot(g, c=color[c])
+    return transformed, fpca
+
+
+def lp_distance(Rcurve1, Rcurve2, p=2):
+    """Lp distance between two response curves
+    @param Rcurve1: Response curve 1.
+    @param Rcurve2: Response curve 2.
+    @param p: Value used to calculate the Lp distance.
+    """
+    diff = np.power(np.abs(Rcurve1 - Rcurve2), p)
+    return np.power(np.sum(diff), 1 / p)
+
+
+def pairwise_distance(Rcurves):
+    n_samples = len(Rcurves)
+    # Calculate distance matrix
+    distance_matrix = np.zeros((n_samples, n_samples))
+    for i in range(n_samples):
+        for j in range(i):
+            dist = lp_distance(Rcurves[i, :], Rcurves[j, :], p=2)
+            distance_matrix[i, j] = dist
+            distance_matrix[j, i] = dist
+    return distance_matrix
+
+
+def t_statistic(RCurves1, RCurves2):
+    """T-statistic for funcional data
+    @param RCurves1: Group 1 of predicted response curves. Shape: (N1, num)
+    @param RCurves2: Group 2 of predicted response curves. Shape: (N2, num)
+    """
+
+    # Calculate mean curves of each groups
+    means1 = np.mean(RCurves1[:, :, 2], axis=0)
+    means2 = np.mean(RCurves2[:, :, 2], axis=0)
+
+    # Calculate standard errors
+    std1 = np.std(RCurves1[:, :, 2], axis=0)
+    std2 = np.std(RCurves2[:, :, 2], axis=0)
+    # Difference
+    diff = np.abs(means1 - means2)
+    # Combined standard error
+    std12 = np.sqrt(std1 ** 2 + std2 ** 2)
+    # Check if means are significantly similar throughout t
+    if np.all(diff - 1.96 * std12 <= 0):
+        return True
+    else:
+        return False
+
+
+def permutation_Ttest(RCurves, rep: int = 10000):
+    Rcurves = RCurves.copy()
+    # Calculate base statistic
+    Tbase = t_statistic(Rcurves[0], Rcurves[1])
+
+    # Start permutations
+    T = np.zeros(rep)
+    Rcurves_joined = np.concatenate((RCurves[0], RCurves[1]))
+    for perm in range(rep):
+        # Permute and separate into two new groups
+        new_grouping = np.random.permutation(np.concatenate((np.zeros(len(RCurves[0]), ), np.ones(len(RCurves[1], )))))
+        GroupA = Rcurves_joined[np.where(new_grouping == 0)[0]]
+        GroupB = Rcurves_joined[np.where(new_grouping == 1)[0]]
+        T[perm] = t_statistic(GroupA, GroupB)
+
+    # Calculate mean pointwise p-value
+    pvalues = np.mean(T >= Tbase)
+    return np.mean(pvalues)
diff --git a/FDA/FDAClustering.py b/FDA/FDAClustering.py
new file mode 100644
index 0000000..1193748
--- /dev/null
+++ b/FDA/FDAClustering.py
@@ -0,0 +1,220 @@
+# import umap
+import FDA.FDA
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import cm
+from sklearn.cluster import AgglomerativeClustering
+
+
+class AgglomerativeClust:
+
+    def __init__(self, Rcurves: np.ndarray):
+        """
+        Agglomerative clustering for functional data.
+        :param Rcurves: Array with shape (N, num, 3) (N resp. curves with a range of num values).
+                    Upper bound response curve: Rcurves[:, :, 0],
+                    Lower bound response curve: Rcurves[:, :, 1],
+                    Predicted response curve: Rcurves[:, :, 2].
+        """
+        # Declare class variables
+        self.fdata = Rcurves
+        self.n_clusters = None
+        self.distance_matrix = None
+        self.n_samples, self.dim = self.fdata.shape
+
+        self.centroids, self.centroids_ub, self.centroids_lb = None, None, None
+        self._centroids_old, self._centroids_ub_old, self._centroids_lb_old = None, None, None
+
+    def pairwise_distance(self):
+        # Calculate distance matrix
+        distance_matrix = np.zeros((self.n_samples, self.n_samples))
+        for i in range(self.n_samples):
+            for j in range(i):
+                dist = FDA.FDA.lp_distance(self.fdata[i, :], self.fdata[j, :], p=2)
+                distance_matrix[i, j] = dist
+                distance_matrix[j, i] = dist
+        self.distance_matrix = distance_matrix
+
+    def add_row_column(self, IndependentGroups, new_cluster_centroid):
+        new_distance_matrix = np.zeros((self.distance_matrix.shape[0] + 1, self.distance_matrix.shape[1] + 1))
+        new_distance_matrix[0:self.distance_matrix.shape[0], 0:self.distance_matrix.shape[1]] = self.distance_matrix
+        for g in range(len(IndependentGroups) - 1):
+            # Calculate distance from previous clusters to new cluster
+            dist = FDA.FDA.lp_distance(np.mean(IndependentGroups[g], axis=0), new_cluster_centroid)
+            new_distance_matrix[-1, g] = dist
+            new_distance_matrix[g, -1] = dist
+        self.distance_matrix = new_distance_matrix
+
+    def fit(self):
+        """Agglomerative algorithm"""
+        self.pairwise_distance()
+        clustering = AgglomerativeClustering(n_clusters=7).fit(self.distance_matrix)
+        nc = len(np.unique(clustering.labels_))
+        plt.figure()
+        color = cm.Set1(np.linspace(0, 1, nc))
+        for g in range(nc):
+            for p in np.where(clustering.labels_ == g)[0]:
+                plt.plot(self.fdata[p, :], c=color[g])
+        plt.figure()
+        nc1, nc2 = 1, 0
+        for ind in np.where(clustering.labels_ == nc1)[0]:
+            plt.plot(self.fdata[ind, :], color='r')
+        for ind in np.where(clustering.labels_ == nc2)[0]:
+            plt.plot(self.fdata[ind, :], color='g')
+
+
+class FuzzyCMeans:
+
+    def __init__(self, Rcurves: np.ndarray, c: int = 2, max_it: int = 100, tol: float = 1e-4, fuzzifier: float = 2):
+        """
+        Fuzzy C-means for functional data.
+        :param Rcurves: Array with shape (N, num, 3) (N resp. curves with a range of num values).
+                    Upper bound response curve: Rcurves[:, :, 0],
+                    Lower bound response curve: Rcurves[:, :, 1],
+                    Predicted response curve: Rcurves[:, :, 2].
+        :param c: Number of desired clusters. Defaults to 2.
+        :param max_it: Maximum number of iterations.
+        :param tol: Tolerance used to compare previous and current centroids.
+        :param fuzzifier: Scalar parameter used to specify the degree of fuzziness in the fuzzy algorithm. Defaults to 2.
+        """
+        # Declare class variables
+        self.fdata = Rcurves[:, :, 2]
+        self.fdata_ub = Rcurves[:, :, 0]
+        self.fdata_lb = Rcurves[:, :, 1]
+        self.widths = (Rcurves[:, :, 0] - Rcurves[:, :, 1])
+        self.n_clusters = c
+        self.n_samples, self.dim = self.fdata.shape
+        self.max_it = max_it
+        self.tol = tol
+        self.fuzzifier = fuzzifier
+
+        self.membership_matrix = np.zeros((self.n_samples, self.n_clusters))
+        self.centroids, self.centroids_ub, self.centroids_lb = None, None, None
+        self._centroids_old, self._centroids_ub_old, self._centroids_lb_old = None, None, None
+
+    def _tolerance(self):
+        """Define tolerance based on variance of the data"""
+        variance = np.var(self.fdata, axis=0)
+        return np.mean(variance * self.tol)
+
+    def _distance_between_centroids(self):
+        """Calculate all possible pairwise distances between centroids and old centroids"""
+        dist = []
+        for n in range(self.n_clusters):
+            dist.append(FDA.FDA.lp_distance(self.centroids[n, :], self._centroids_old[n, :], p=2))
+        return np.array(dist)
+
+    def _prediction_from_membership(self):
+        return np.argmax(self.membership_matrix, axis=1, )
+
+    def fuzzy_silhouette(self):
+        labels = self._prediction_from_membership()
+
+        # Calculate silhouette coefficient
+        s = np.zeros(self.n_samples)
+        for ns in range(self.n_samples):
+            # Average distance of object ns to all other objects belonging to the same cluster
+            indices = np.where(labels == labels[ns])[0]  # Indices of samples with the same label
+            dist = 0
+            for ind in indices:
+                if ind != ns:
+                    dist += FDA.FDA.lp_distance(self.fdata[ns, :], self.fdata[ind, :], p=2)
+            apj = dist / (len(indices) - 1)
+            # Average distance of this object to all objects belonging to another cluster
+            min_dist = np.infty
+            for nc in range(self.n_clusters):
+                if nc != labels[ns]:
+                    indices = np.where(labels == nc)[0]  # Indices of samples with the same label
+                    dist = 0
+                    for ind in indices:
+                        dist += FDA.FDA.lp_distance(self.fdata[ns, :], self.fdata[ind, :], p=2)
+                    dist = dist / (len(indices))
+                if dist < min_dist:
+                    min_dist = dist
+            bpj = min_dist
+            # Silhouette
+            s[ns] = (bpj - apj) / np.maximum(apj, bpj)
+
+        # Calculate fuzzy silhouette
+        nom, den = 0, 0
+        for ns in range(self.n_samples):
+            # Get two largest memberships
+            u = self.membership_matrix[ns, :]
+            [uqj, upj] = np.sort(u)[-2:]
+            nom += (upj - uqj) * s[ns]
+            den += (upj - uqj)
+
+        FS = nom / den
+        return FS
+
+    def fit(self, random_state=7):
+        """Fuzzy c-means algorithm"""
+        repetitions = 0
+
+        # Initialize centroids
+        c_indices = np.arange(self.n_samples)
+        np.random.seed(random_state)
+        np.random.shuffle(c_indices)
+        self.centroids = self.fdata[c_indices]
+        self.centroids_ub = self.fdata_ub[c_indices]
+        self.centroids_lb = self.fdata_lb[c_indices]
+        self._centroids_old = self.centroids.copy()
+        self._centroids_ub_old = self.centroids_ub.copy()
+        self._centroids_lb_old = self.centroids_lb.copy()
+
+        tolerance = self._tolerance()
+
+        # Start iterations
+        while repetitions == 0 \
+                or (not np.all(self._distance_between_centroids() < tolerance) and repetitions < self.max_it):
+            self._centroids_old = self.centroids.copy()
+            self._centroids_ub_old = self.centroids_ub.copy()
+            self._centroids_lb_old = self.centroids_lb.copy()
+
+            # Compute distance from all curves to centroids
+            distances_to_centroids = np.zeros((self.n_samples, self.n_clusters))
+            for nc in range(self.n_clusters):
+                for ns in range(self.n_samples):
+                    dist = FDA.FDA.lp_distance(self.centroids[nc, :], self.fdata[ns, :], p=2)
+                    distances_to_centroids[ns, nc] = dist
+
+            ################
+            # Perform update
+            ################
+            # Divisions by zero allowed
+            with np.errstate(divide='ignore'):
+                distances_to_centers_raised = (distances_to_centroids ** (2 / (1 - self.fuzzifier)))
+
+            # Divisions infinity by infinity allowed
+            with np.errstate(invalid='ignore'):
+                self.membership_matrix[:, :] = (
+                        distances_to_centers_raised / np.sum(distances_to_centers_raised, axis=1,
+                                                             keepdims=True, ))
+            # inf / inf divisions should be 1 in this context
+            self.membership_matrix[np.isnan(self.membership_matrix)] = 1
+
+            membership_matrix_raised = np.power(self.membership_matrix, self.fuzzifier, )
+
+            slice_denominator = (
+                    (slice(None),) + (np.newaxis,) * (self.fdata.ndim - 1))
+            # Compute the new centroids and their corresponding PIs
+            self.centroids = (
+                    np.einsum('ij,i...->j...', membership_matrix_raised, self.fdata, ) /
+                    np.sum(membership_matrix_raised, axis=0)[slice_denominator])
+            self.centroids_ub = (
+                    np.einsum('ij,i...->j...', membership_matrix_raised, self.fdata_ub, ) /
+                    np.sum(membership_matrix_raised, axis=0)[slice_denominator])
+            self.centroids_lb = (
+                    np.einsum('ij,i...->j...', membership_matrix_raised, self.fdata_lb, ) /
+                    np.sum(membership_matrix_raised, axis=0)[slice_denominator])
+
+            repetitions += 1
+
+        # Obtain labels
+        labels = self._prediction_from_membership()
+        highest_memberships = np.array([m[l] for m, l in zip(self.membership_matrix, labels)])
+        # Get indices for each cluster
+        indices = []
+        for nc in range(self.n_clusters):
+            indices.append(np.where(labels == nc)[0])
+        return labels, indices, highest_memberships
diff --git a/FDA/__init__.py b/FDA/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/FeatureResponsivity.py b/FeatureResponsivity.py
new file mode 100644
index 0000000..82e63ec
--- /dev/null
+++ b/FeatureResponsivity.py
@@ -0,0 +1,348 @@
+import os
+import sys
+import time
+import utils
+import torch
+import numpy as np
+from FDA import FDA
+from skfda import FDataGrid
+from tqdm import trange
+import matplotlib.pyplot as plt
+from DataLoader import DataLoader
+from models.NNModel import NNModel
+from itertools import combinations
+from sklearn.model_selection import KFold
+from MOO import MixedVarsMOO, response_curves
+from multiprocessing.pool import ThreadPool
+from pymoo.core.problem import StarmapParallelization
+
+from pymoo.optimize import minimize
+from pymoo.algorithms.moo.nsga2 import NSGA2
+# from pymoo.visualization.scatter import Scatter
+from pymoo.core.mixed import MixedVariableMating, MixedVariableSampling, MixedVariableDuplicateElimination
+
+
+class FeatureResponsivity:
+
+    def __init__(self, dataset='Synth'):
+        """
+        @param dataset: Dataset name. Options: 'Insurance'.
+        """
+        self.dataset = dataset
+        self.min_resp, self.max_resp = 0, 1
+
+        # Load dataset
+        dataLoader = DataLoader(name=dataset)
+        self.X, self.Y = dataLoader.X, dataLoader.Y
+        self.types, self.names, self.modelType = dataLoader.types, dataLoader.names, dataLoader.modelType
+
+        # Load Model
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model = self.reset_model()
+
+    def reset_model(self):
+        if self.X.ndim == 2:
+            return NNModel(device=self.device, nfeatures=self.X.shape[1], method='MCDropout',
+                           modelType=self.modelType, dataset=self.dataset)
+        else:
+            return NNModel(device=self.device, nfeatures=self.X.shape[1:], method='MCDropout',
+                           modelType=self.modelType, dataset=self.dataset)
+
+    def impact(self, s: int, epsi: list, replace=False):
+        """Estimate the impact of each feature on the responsivity of another feature.
+        @param s: Index of the feature whose responsivity w.r.t response variable will be assessed.
+        @param epsi: List of tolerance errors that will be tested.
+        @param replace: If True, overwrite existing results.
+        NOTE: This implementation assumes that the models are already trained using Trainer.py and 10x1 CV"""
+
+        kfold = KFold(n_splits=10, shuffle=True, random_state=13)  # Initialize kfold object
+        # If the folder does not exist, create it
+        folder = "CVResults//" + self.dataset + "//" + 'MCDropout'
+        if not os.path.exists("CVResults//" + self.dataset):
+            sys.exit("The model does not exist. Use Trainer.py using 10x1 CV first.")
+        folder2 = folder + "//Responsivity_feature-" + str(s)
+        if not os.path.exists(folder2):
+            os.mkdir(folder2)
+
+        iterator = kfold.split(self.X)
+        ntrain = 1
+        Ids_cv = np.zeros((len(self.types) - 1, 11, len(epsi)))  # Save cross-validation results
+        indexes = [fe for fe in np.arange(len(self.types)) if fe != s]  # List of variable features (!= s)
+        Ids_cv[:, 0, :] = np.repeat(np.reshape(indexes, (len(indexes), 1)), len(epsi),
+                                    axis=1)  # First column saves the feature indexes
+        # Create list with all possible combination of feature indexes (up to a max. of 4 features)
+        indx = np.arange(len(indexes))
+        feat_combs = []
+        for v in range(2, 5):
+            feat_combs += list(combinations(indx, v))
+        combs_matrix = np.zeros((len(feat_combs), 10, len(epsi)))
+
+        # Iterate through each partition
+        samples = None
+        for first, second in iterator:
+            print("\n******************************")
+            print("Analyzing fold: " + str(ntrain))
+            print("******************************")
+            impact_results_path = [folder2 + "//Local_Ids_s-" + str(s) + "_sample_matrix_fold-" + str(ntrain) +
+                                   "_epsi-" + str(np.round(eps, 1)) + '.npy' for eps in epsi]
+            train = np.array(first)
+            # Normalize using the training set. NOTE: These are the same partitions used during training (same seed).
+            Xtrain, means, stds = utils.normalize(self.X[train])
+
+            # Define path where the model is saved and load it
+            filepath = folder + "//weights-MCDropout-" + self.dataset + "-" + str(ntrain)
+            self.model.loadModel(filepath)
+
+            # Get minimum and maximum values, and count how many possible values each feature has
+            if self.X.ndim == 2:
+                xmin, xmax, num = np.zeros((self.X.shape[1])), np.zeros((self.X.shape[1])), np.zeros((self.X.shape[1]))
+                xmin_orig, xmax_orig = np.zeros((self.X.shape[1])), np.zeros((self.X.shape[1]))
+                for f in range(self.X.shape[1]):
+                    xmin[f], xmax[f], num[f] = np.min(Xtrain[:, f]), np.max(Xtrain[:, f]), len(np.unique(Xtrain[:, f]))
+                    xmin_orig[f], xmax_orig[f] = np.min(self.X[train][:, f]), np.max(self.X[train][:, f])
+            else:
+                xmin, xmax, num = np.zeros((self.X.shape[2])), np.zeros((self.X.shape[2])), np.zeros((self.X.shape[2]))
+                xmin_orig, xmax_orig = np.zeros((self.X.shape[2])), np.zeros((self.X.shape[2]))
+                for f in range(self.X.shape[2]):
+                    xmin[f], xmax[f], num[f] = np.min(Xtrain[:, :, f, :, :]), np.max(Xtrain[:, :, f, :, :]), \
+                        len(np.unique(Xtrain[:, :, f, :, :]))
+                    xmin_orig[f], xmax_orig[f] = np.min(self.X[train][:, :, f, :, :]), \
+                        np.max(self.X[train][:, :, f, :, :])
+                if self.dataset != 'Synth':
+                    xmax[s] = (150 - means[s]) / stds[s]
+                    xmin[s] = (0 - means[s]) / stds[s]
+                    xmin_orig[s] = 0
+                    xmax_orig[s] = 150
+
+            ###########################################
+            # MOO Problem
+            ###########################################
+            samples = len(train)
+            if len(train) > 500:  # Limit the experiment to 500 samples
+                samples = 500
+            Ids = np.zeros((samples, len(self.types) - 1, len(epsi)))
+
+            # If not all results are already saved, generate response curves
+            if (not np.all([os.path.exists(path) for path in impact_results_path])) or replace:
+                # Get response curves for all points in the dataset:
+                print("Generating response curves for the entire training set...")
+                start = time.time()
+                Rcurves = response_curves(model=self.model, X=Xtrain, s=s, xmin=xmin[s], xmax=xmax[s], num=num[s],
+                                          MC=True)
+                end = time.time()
+                print("It took " + str(end - start) + " s. to generate all the response curves of the s-th feature.")
+
+                ###########################################
+                # PRE-PROCESSING
+                ###########################################
+                # Response curves alignment
+                Rcurves = FDA.function_alignment(Rcurves)
+                # Apply FPCA
+                Rcurves_transformed, fpca = FDA.F_PCA(Rcurves)
+
+                ###########################################
+                # CFE ANALYSIS
+                ###########################################
+                # Analyze each sample of the dataset
+                for i in trange(samples):
+                    x_orig = self.X[train][i, :]
+                    # Calculate mutation probability of each variable given current values
+                    probs = []
+                    for f in range(len(self.types)):
+                        if self.types[f] in ['binary', 'categorical']:
+                            # Count how many samples exist in the dataset with different values than the current one
+                            num_diff = len(np.where(self.X[train][:, f] != self.X[train][i, f])[0])
+                            probs.append(num_diff / len(train))
+                        else:
+                            probs.append(None)  # If numerical, use default probability
+
+                    # Find maximum distance between sample and other samples in the transformed space
+                    x_orig_transformed = fpca.transform(FDataGrid(data_matrix=Rcurves[i],
+                                                                  grid_points=np.linspace(0, 1, 50)))
+                    ds = []
+                    for t in Rcurves_transformed:
+                        ds.append(FDA.lp_distance(x_orig_transformed, t))
+                    epsi_max = np.max(ds)
+
+                    curves, x_norm = [], None
+                    for ne, eps in enumerate(epsi):
+                        time.sleep(0.05)
+                        print("\n**************************")
+                        print("Analyzing epsilon=" + str(np.round(eps, 2)))
+                        print("**************************")
+                        n_threads = 20
+                        pool = ThreadPool(n_threads)
+                        runner = StarmapParallelization(pool.starmap)
+
+                        # Define multi-objective optimization problem
+                        if eps > epsi_max:
+                            eps = epsi_max
+                        moo = MixedVarsMOO(x_orig=x_orig, model=self.model, epsi=eps, transformer=fpca,
+                                           probs=probs, resp_orig=Rcurves[i], s=s, types=self.types,
+                                           xmin=xmin_orig, elementwise_runner=runner,
+                                           xmax=xmax_orig, minS=xmin[s], maxS=xmax[s], num=num[s], stats=[means, stds])
+
+                        # Optimize using NSGA-II
+                        algorithm = NSGA2(pop_size=50,
+                                          sampling=MixedVariableSampling(),
+                                          mating=MixedVariableMating(
+                                              eliminate_duplicates=MixedVariableDuplicateElimination()),
+                                          eliminate_duplicates=MixedVariableDuplicateElimination(), )
+                        res = minimize(moo, algorithm, ('n_gen', 80), seed=1, verbose=False)
+
+                        # Select best solution
+                        sols = res.F
+                        sols[:, 0] = np.round(sols[:, 0], 2)
+                        bestf1 = np.min(sols[:, 0])
+                        f1sols = sols[(sols[:, 0] == bestf1)]  # Select solutions that produced the highest resp. change
+                        bestf2 = np.min(f1sols[:, 1])  # The solution that required to change the fewer features
+                        best_sol = np.where((sols[:, 0] == bestf1) & (sols[:, 1] == bestf2))[0][0]
+                        # Assess which features changed the most
+                        if self.modelType == 'NN':
+                            x_opt = np.array(
+                                [res.X[best_sol][f"x{k:02}"] if k != s else x_orig[s] for k in range(len(self.types))])
+                        else:
+                            x_opt = np.array([res.X[best_sol][f"x{k:02}"] if k != s else x_orig[0, s, 2, 2] for k in
+                                              range(len(self.types))])
+                            diff = x_orig[0, :, 2, 2] - x_opt
+                            diff = np.multiply(np.ones(x_orig.shape), np.reshape(diff, (1, x_orig.shape[1], 1, 1)))
+                            x_opt = x_orig[0, :, :, :] - diff
+                        dist = utils.gower(x_orig, x_opt, types=self.types, ranges=(xmax_orig - xmin_orig))
+                        Ids[i, :, ne] = np.delete(dist, s)  # Remove feature s
+                        print(str(Ids[i, :, ne] > 0.01))
+                        if self.modelType == 'NN':
+                            x_norm = np.reshape(Xtrain[i, :], (1, len(self.types)))
+                        else:
+                            x_norm = Xtrain[i:i + 1, :, :, :, :]
+                        x_norm2 = utils.applynormalize(np.reshape(x_opt, x_norm.shape), means, stds)
+                        curve2 = response_curves(self.model, x_norm2, s, xmin=xmin[s], xmax=xmax[s], num=num[s]) \
+                                  / 10 * np.max(self.Y[train])
+                        curve2 = FDA.smooth_curves(curve2)
+                        curves.append(FDA.function_alignment(curve2))
+
+                    #############################################################
+                    # PLOT Diference between original and resulting response curve
+                    #############################################################
+                    # fig = plt.figure()
+                    # ax = fig.add_subplot(111)
+                    # curve = response_curves(self.model, x_norm, s, xmin=xmin[s], xmax=xmax[s], num=num[s]) \
+                    #         / 10 * np.max(self.Y[train])
+                    # curve = FDA.function_alignment(curve)
+                    # curve = FDA.smooth_curves(curve)
+                    # xlabels = np.linspace(xmin_orig[s], xmax_orig[s], curve.shape[1])
+                    # ax.plot(xlabels, curve[0, :], linewidth=7, label=r'$\tilde{R}(\mathbf{x})$')
+                    # for ne, eps in enumerate(epsi):
+                    #     ax.plot(xlabels, curves[ne][0, :],
+                    #             linewidth=7, label=r"$\tilde{R}(\mathbf{x}')$. $ϵ = $" + str(eps))
+                    # ax.locator_params(axis='x', nbins=2)
+                    # plt.xticks([xmin_orig[s], xmax_orig[s]], ['$x_s^{(min)}$', '$x_s^{(max)}$'], fontsize=16)
+                    # plt.yticks(fontsize=16)
+                    # plt.xlabel('$x_s$', fontsize=20)
+                    # plt.ylabel('$\hat{y}$', fontsize=20)
+                    # plt.legend(fontsize=16)
+                    # ax.set_aspect(30 / 1)  # For fields A and B
+                    # ax.set_aspect(1 / 6)  # For the synthetic dataset
+                    # plt.xlabel('Nitrogen Rate')
+                    # plt.ylabel('Predicted Yield')
+                    # plt.pause(0.05)
+            else:
+                for npath, path in enumerate(impact_results_path):
+                    Ids[:, :, npath] = np.load(path)
+
+            ###################
+            # Analyze results
+            ###################
+            for ne, eps in enumerate(epsi):
+                Ids_bin = Ids[:, :, ne] > 0.005  # Hihglight the features that were changed for each sample
+                plt.figure()
+                plt.imshow(Ids_bin, aspect='auto')
+                plt.xticks(np.arange(len(indexes)), [self.names[indexes[v]] for v in range(len(indexes))])
+                plt.pause(0.05)
+                # Save results
+                np.save(impact_results_path[ne], Ids[:, :, ne])
+                # Create an array of lists that stores the indexes of the features that changed
+                Ilist = np.frompyfunc(list, 0, 1)(np.empty((Ids.shape[0],), dtype=object))
+                for n in range(Ids.shape[0]):
+                    Ilist[n] = list(np.where(Ids_bin[n])[0])
+                # Calculate the percentage of times that each variable changed
+                percentage = [100 * np.sum(Ids_bin[:, v]) / Ids.shape[0] for v in range(Ids.shape[1])]
+                # Retrieve the unique combination of changing variables and how many times they appeared
+                combs, comb_num = np.unique(Ilist, return_counts=True)
+                sorted_comb_num = np.argsort(comb_num)
+                sorted_combinations = combs[sorted_comb_num]  # Combinations of features sorted by repetition
+                if len(sorted_combinations) >= 5:
+                    top5combinations = sorted_combinations[-5:][::-1]
+                else:
+                    top5combinations = sorted_combinations[::-1]
+
+                # Update number of multiple feature combinations
+                for vi, v in enumerate(feat_combs):
+                    combs_matrix[vi, ntrain - 1, ne] = 0
+                    if list(v) in list(combs):  # If the combination appeared, count how many times
+                        p = [p for p, vt in enumerate(combs) if vt == list(v)][0]
+                        combs_matrix[vi, ntrain - 1, ne] = comb_num[p]
+
+                # Print results
+                print("**************************")
+                print("Results for epsilon=" + str(np.round(eps, 2)))
+                print("**************************")
+                for v in range(len(indexes)):
+                    print('\033[0m' + "The feature " + str(indexes[v]) + " (" + self.names[
+                        indexes[v]] + ") was modified " +
+                          '\033[1m' + str(np.round(percentage[v], 2)) + " % of the time." + '\033[0m')
+                print("\nThe top-5 most repeated combination of passive features was:")
+                for v in range(len(top5combinations)):
+                    print(
+                        "Combination " + str(v) + " : " + str([self.names[indexes[vi]] for vi in top5combinations[v]]))
+                print("\nThe top-5 most repeated combination of multiple passive features was:")
+
+                Ids_cv[:, ntrain, ne] = percentage
+            ntrain += 1
+
+        # Count the most frequent feature combinations
+        for ne, eps in enumerate(epsi):
+            print("\nThe top-5 most repeated combination of multiple passive features for epsilon = " + str(eps))
+            combs_matrixn = combs_matrix[:, :, ne] / samples * 100
+            combs_percentage = np.mean(combs_matrixn, axis=1)
+            combs_std = np.std(combs_matrixn, axis=1)
+            combs_indx = np.argsort(combs_percentage)
+            sorted_combs = np.array(feat_combs)[combs_indx]  # Combinations of features sorted by repetition
+            if len(sorted_combs) >= 5:
+                top5combinations = sorted_combs[-5:][::-1]
+            else:
+                top5combinations = sorted_combs[::-1]
+            for vi, v in enumerate(range(len(top5combinations))):
+                print(
+                    "Combination " + str(v) + " : " + str([self.names[indexes[vi]] for vi in top5combinations[v]]) +
+                    ". Percentage = " + str(combs_percentage[combs_indx[-vi - 1]]) + " +/- " + str(
+                        combs_std[combs_indx[-vi - 1]]))
+        # Plot results of CV
+        for ne, eps in enumerate(epsi):
+            plt.figure()
+            plt.imshow(Ids_cv[:, 1:, ne].T, aspect='auto')
+            plt.xticks(np.arange(len(indexes)), [self.names[indexes[v]] for v in range(len(indexes))])
+
+            np.save(folder2 + "//Global_Ids_s-" + str(s) + "_epsi-" + str(np.round(eps, 1)) + "_fold-" + str(ntrain),
+                    Ids_cv[:, :, ne])
+            # Save metrics
+            file_name = folder2 + "//Global_Ids_s-" + str(s) + "_epsi-" + str(np.round(eps, 1)) + ".txt"
+            with open(file_name, 'w') as x_file:
+                for f in range(Ids_cv.shape[0]):
+                    head = "Global Impact of feature " + str(indexes[f]) + " (" + self.names[indexes[f]] + \
+                           ") on responsivity of feature " + str(s) + " = "
+                    x_file.write(
+                        head + "%.4f (+/- %.4f)" % (
+                            float(np.mean(Ids_cv[f, 1:, ne])), float(np.std(Ids_cv[f, 1:, ne]))))
+                    x_file.write('\n')
+
+
+if __name__ == '__main__':
+    name = 'Synth'
+    fresp = FeatureResponsivity(dataset=name)
+    fresp.impact(s=0, epsi=[0.4, 0.6, 0.8], replace=True)
+    name = 'FieldA'
+    fresp = FeatureResponsivity(dataset=name)
+    fresp.impact(s=0, epsi=[0.6, 0.8, 1], replace=True)
+    name = 'FieldB'
+    fresp = FeatureResponsivity(dataset=name)
+    fresp.impact(s=0, epsi=[0.6, 0.8, 1], replace=True)
diff --git a/MOO.py b/MOO.py
new file mode 100644
index 0000000..bd786c9
--- /dev/null
+++ b/MOO.py
@@ -0,0 +1,217 @@
+import sys
+import numpy as np
+import skfda.preprocessing.dim_reduction.feature_extraction
+
+import FDA.FDA
+from utils import gower, applynormalize
+from pymoo.core.problem import ElementwiseProblem
+from pymoo.core.variable import Real, Integer, Binary
+
+
+#############################################################################
+# RESPONSE CURVE GENERATION
+#############################################################################
+
+def response_curves(model, X, s, xmin, xmax, num, MC=True):
+    """Get the response curves of the s-th feature for each input sample
+    @param model: Pytorch model.
+    @param X: Data samples.
+    @param s: Index of the feature whose responsivity will be assessed.
+    @param xmin: Minimum value each feature can take.
+    @param xmax: Maximum value each feature can take.
+    @param num: Number of possible values the s-th feature can take.
+    @param MC: If True, use MC-Dropout.
+    """
+    if MC:  # Activate dropout unit
+        MCsamples = 1
+    else:
+        MCsamples = 1
+    if num > 50:
+        num = 50
+
+    Y = np.zeros((X.shape[0], num))
+    for n, x in enumerate(X):
+        if model.modelType == 'NN':
+            xvector = np.repeat(np.reshape(x, (1, len(x))), num, axis=0)
+            for i, xs in enumerate(np.linspace(start=xmin, stop=xmax, num=num)):
+                xvector[i, s] = xs  # Replace the s-th value
+        else:
+            xvector = np.repeat(np.reshape(x, (1, x.shape[0], x.shape[1], x.shape[2], x.shape[3])), num, axis=0)
+            for i, xs in enumerate(np.linspace(start=xmin, stop=xmax, num=num)):
+                xvector[i, 0, s, :, :] = xs  # Replace the s-th value
+            xvector = np.repeat(xvector, MCsamples, axis=0)
+
+        # Pass through the NN
+        y = model.evaluateFoldUncertainty(valxn=xvector, maxs=None, mins=None, batch_size=2000, MC_samples=1)
+        if model.modelType == 'NN':
+            Y[n, :] = np.mean(y, axis=-1)
+        else:
+            for t in range(num):
+                y2 = [yy for yy in y[t * MCsamples:(t + 1) * MCsamples, 2, 2, :] if yy != 0]
+                Y[n, t] = np.mean(y2)
+    return Y
+
+
+#############################################################################
+# OBJECTIVE FUNCTIONS
+#############################################################################
+
+def L1(new_resp: np.ndarray, old_resp: np.ndarray, fpca: skfda.preprocessing.dim_reduction.feature_extraction.FPCA,
+       threshold: int):
+    """Objective function 1: Modify response curve"""
+    # Convert curves into FDatagrid
+    new_resp = skfda.FDataGrid(data_matrix=new_resp, grid_points=np.linspace(0, 1, len(new_resp)))
+    old_resp = skfda.FDataGrid(data_matrix=old_resp, grid_points=np.linspace(0, 1, len(old_resp)))
+    # Transform curves using fPCA
+    new_resp = fpca.transform(new_resp)
+    old_resp = fpca.transform(old_resp)
+    # Calculate L2 distance in the new space
+    d = FDA.FDA.lp_distance(new_resp, old_resp)
+    if d > threshold:
+        d = threshold
+    return - d
+
+
+def L2(x_orig, x_counter, types, ranges):
+    """Objective function 2: Modify as few features as possible"""
+    comp = []
+    for i in range(len(types)):
+        if x_orig.ndim <= 2:
+            if types[i] == 'real':
+                comp.append(int(np.abs(x_orig[i] - x_counter[i]) / ranges[i] > 0.01))
+            else:
+                comp.append(int(x_orig[i] != x_counter[i]))
+        else:
+            if types[i] == 'real':
+                comp.append(int(np.mean(np.abs(x_orig[0][i] - x_counter[0][i])) / ranges[i] > 0.01))
+            else:
+                comp.append(int(x_orig[0][i] != x_counter[0][i]))
+
+    return np.sum(comp)
+
+
+def L3(new_resp, Resp_orig):
+    """Objective function 3: Counterfactual explanations should have sound feature value combinations"""
+    distances = [FDA.FDA.lp_distance(new_resp, resp) for resp in Resp_orig]
+    return sorted(distances)[-1]
+
+
+def L4(x_new, x_old, types, ranges):
+    """Objective function 4: Counterfactual explanations should be close to the original feature values"""
+    return np.mean(gower(x_new, x_old, types, ranges))
+
+
+#############################################################################
+# MULTI-OBJECTIVE OPTIMIZATION
+#############################################################################
+
+class MixedVarsMOO(ElementwiseProblem):
+
+    def __init__(self, x_orig, transformer, model, epsi, probs, s, types, xmin, xmax, minS, maxS, num, stats,
+                 resp_orig=None, centroids=None, **kwargs):
+        """Initialize Multi-Objective Optimization object.
+        @param x_orig: Original input values
+        @param model: Pytorch model.
+        @param epsi: Tolerance error.
+        @param probs: Probability of mutation of each feature
+        @param s: Index of the variable whose responsitivity is being analyzed.
+        @param types: Array containing the types of input variables.
+        @param xmin: Minimum original value each feature can take.
+        @param xmax: Maximum original value each feature can take.
+        @param minS: Minimum transformed value the s-th feature can take.
+        @param maxS: Maximum transformed original value the s-th feature can take.
+        @param num: Number of possible values the s-th feature can take.
+        @param stats: Statistics used to apply z-score normalization before passing the input through the model.
+        @param resp_orig: Original response curve.
+        @param centroids: Centroid curves that will be used for comparison.
+        """
+        # Class variables
+        self.x_orig = x_orig
+        self.transformer = transformer
+        self.model = model
+        self.epsi = epsi
+        # self.XObs = XObs
+        self.probs = probs
+        self.types = types
+        self.s = s
+        self.minS = minS
+        self.maxS = maxS
+        self.num = num
+        self.stats = stats
+        self.centroids = centroids
+        self.ranges = xmax - xmin
+
+        # Calculate original responsivity
+        if model.modelType == 'NN':
+            x_norm = applynormalize(np.reshape(self.x_orig, (1, len(self.types))), self.stats[0], self.stats[1])
+        else:
+            x_norm = applynormalize(np.reshape(self.x_orig, (1, self.x_orig.shape[0], self.x_orig.shape[1],
+                                                             self.x_orig.shape[2], self.x_orig.shape[3])),
+                                    self.stats[0], self.stats[1])
+        self.resp_orig = resp_orig
+        if resp_orig is None:
+            self.resp_orig = self.ind_responsivity(x_norm, s, minS, maxS, self.num, MC=True)
+
+        # Declare optimization variables
+        variables = dict()
+        for k in [i for i in range(len(self.types)) if i != s]:  # Consider all variables except the s-th variable
+            if self.types[k] == 'real':
+                var = Real(bounds=(xmin[k], xmax[k]))
+            elif self.types[k] == 'integer':
+                var = Integer(bounds=(int(xmin[k]), int(xmax[k])))
+            elif self.types[k] == 'binary':
+                var = Binary()
+                var.prob = self.probs[k]  # Assign the probability of mutation of the variable
+                ########################################################################################################
+                # NOTE: Using pymoo 0.6.0
+                # * The instance variable "prob" needs to be added to the pymoo.core.Variable class (self.prob=None)
+                #
+                # * The "pymoo.core.mutation.Mutation.get_prob_var" is modified as follows to support different
+                #   individual mutation probabilities:
+                #   if all([v.prob is not None for v in problem.vars]):  # Check if indiv. probabilities were assigned
+                #       prob_var = [v.prob for v in problem.vars]
+                #   else:
+                #       prob_var = self.prob_var if self.prob_var is not None else min(0.5, 1 / problem.n_var)
+                # * Finally, the pymoo.operators.mutation.bitflip.BitflipMutation._do method needs to consider different
+                #   mutation probabilities as well so the following:
+                #   prob_var = self.get_prob_var(problem, size=(len(X), len(self.get_prob_var(problem))))
+                ########################################################################################################
+            else:
+                sys.exit("Only accepting real, integer and binary values for now.")
+            variables[f"x{k:02}"] = var
+
+        super().__init__(vars=variables, n_obj=3)  # , n_ieq_constr=1)
+
+    def ind_responsivity(self, x, s, xmin, xmax, num, MC=True):
+        """Evaluate individual responsivity of an input x"""
+        # Obtain N-response curve
+        Rcurve = response_curves(self.model, x.copy(), s, xmin, xmax, num, MC=MC)
+        # Curve alignment
+        return FDA.FDA.function_alignment(Rcurve)[0]
+
+    def _evaluate(self, x, out, *args, **kwargs):
+        # Generate response curve of the counterfactual
+        if self.model.modelType == 'NN':
+            # Complete input vector using a dummy value for the s-th feature
+            x = np.array([x[f"x{k:02}"] if k != self.s else self.x_orig[self.s] for k in range(len(self.types))])
+            x_norm = applynormalize(np.reshape(x, (1, len(self.types))), self.stats[0], self.stats[1])
+        else:
+            x = np.array([x[f"x{k:02}"] if k != self.s else self.x_orig[0, self.s, 2, 2] for k in range(len(self.types))])
+            diff = self.x_orig[0, :, 2, 2] - x
+            diff = np.multiply(np.ones(self.x_orig.shape), np.reshape(diff, (1, self.x_orig.shape[1], 1, 1)))
+            x = self.x_orig[0, :, :, :] - diff
+            # for k in range(len(self.types)):
+
+            x_norm = applynormalize(np.reshape(x, (1, self.x_orig.shape[0], self.x_orig.shape[1],
+                                                             self.x_orig.shape[2], self.x_orig.shape[3])),
+                                    self.stats[0], self.stats[1])
+        resp = self.ind_responsivity(x_norm, self.s, self.minS, self.maxS, self.num, MC=True)
+        resp = FDA.FDA.smooth_curves(resp)[0, :]
+
+        # Calculate first objective
+        f1 = L1(resp, self.resp_orig, fpca=self.transformer, threshold=self.epsi)
+        # Calculate second objective
+        f2 = L2(x, self.x_orig, self.types, self.ranges)
+        # Calculate THIRD objective
+        f3 = L4(x, self.x_orig, self.types, self.ranges)
+        out["F"] = [f1, f2, f3]
diff --git a/Trainer.py b/Trainer.py
new file mode 100644
index 0000000..d7dc1af
--- /dev/null
+++ b/Trainer.py
@@ -0,0 +1,207 @@
+import os
+import sys
+import time
+import utils
+import torch
+import pickle
+import numpy as np
+# import matplotlib.pyplot as plt
+from DataLoader import DataLoader
+from models.NNModel import NNModel
+from sklearn.model_selection import KFold
+from sklearn.model_selection import train_test_split
+
+
+class PIGenerator:
+
+    def __init__(self, dataset='Synth', method='DualAQD'):
+        """Class used for training the NNs
+        :param dataset: Name of the dataset. Options: 'Synth', 'FieldA', or 'FieldB'/
+        :param method: Method used for generating prediction intervals. Options: 'MC-Dropout' or 'DualAQD'. For the CFE
+        analysis, use 'MC-Dropout' only.
+        """
+        self.dataset = dataset
+        self.method = method
+        # Load dataset
+        dataLoader = DataLoader(name=dataset)
+        self.X, self.Y, self.types, self.names, self.modelType = dataLoader.X, dataLoader.Y, dataLoader.types, \
+                                                                 dataLoader.names, dataLoader.modelType
+        # Initialize kfold object
+        self.kfold = KFold(n_splits=10, shuffle=True, random_state=13)
+        # Initialize model
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model = self.reset_model()
+
+    def reset_model(self):
+        if self.X.ndim == 2:
+            return NNModel(device=self.device, nfeatures=self.X.shape[1], method=self.method,
+                           modelType=self.modelType, dataset=self.dataset)
+        else:
+            return NNModel(device=self.device, nfeatures=self.X.shape[1:], method=self.method,
+                           modelType=self.modelType, dataset=self.dataset)
+
+    def train(self, crossval='10x1', batch_size=16, epochs=500, alpha_=0.01, printProcess=True, val=False):
+        """Train using cross validation
+        @param crossval: Type of cross-validation. Options: '10x1' or '5x2'
+        @param batch_size: Mini batch size. It is recommended a small number, like 16
+        @param epochs: Number of training epochs
+        @param alpha_: Hyperparameter(s) used by the selected PI generation method
+        @param printProcess: If True, print the training process (loss and validation metrics after each epoch)
+        @param val: If True, just perform validation, not training."""
+        # Create lists to store metrics
+        cvmse, cvpicp, cvmpiw, cvdiffs = [], [], [], []
+
+        # If the folder does not exist, create it
+        folder = "CVResults//" + self.dataset + "//" + self.method
+        if not os.path.exists("CVResults//" + self.dataset):
+            os.mkdir("CVResults//" + self.dataset)
+        if not os.path.exists(folder):
+            os.mkdir(folder)
+
+        if crossval == "10x1":
+            iterator = self.kfold.split(self.X)
+            print("Using 10x1 cross-validation for this dataset")
+        elif crossval == "5x2":
+            # Choose seeds for each iteration is using 5x2 cross-validation
+            seeds = [13, 51, 137, 24659, 347, 436, 123, 64, 958, 234]
+            iterator = enumerate(seeds)
+            print("Using 5x2 cross-validation for this dataset")
+        else:
+            sys.exit("Only '10x1' and '5x2' cross-validation are permited.")
+
+        ntrain = 1
+        # Iterate through each partition
+        for first, second in iterator:
+            if ntrain >= 1:
+                if crossval == '10x1':
+                    train = np.array(first)
+                    test = np.array(second)
+                else:
+                    train, test = train_test_split(range(len(self.X)), test_size=0.50, random_state=second)
+                    train = np.array(train)
+                    test = np.array(test)
+
+                print("\n******************************")
+                print("Analyzing fold: " + str(ntrain))
+                print("******************************")
+                # Normalize using the training set
+                Xtrain, means, stds = utils.normalize(self.X[train])
+                Ytrain, maxs, mins = utils.minMaxScale(self.Y[train])
+                Xval = utils.applynormalize(self.X[test], means, stds)
+                Yval = utils.applyMinMaxScale(self.Y[test], maxs, mins)
+
+                # Define path where the model will be saved
+                filepath = folder + "//weights-" + self.method + "-" + self.dataset + "-" + str(ntrain)
+
+                # Train the model using the current training-validation split
+                self.model = self.reset_model()
+                mse, PICP, MPIW = None, None, None
+                if not val:
+                    _, _, _, mse, PICP, MPIW = self.model.trainFold(Xtrain=Xtrain, Ytrain=Ytrain, Xval=Xval, Yval=Yval,
+                                                                    batch_size=batch_size, epochs=epochs,
+                                                                    filepath=filepath, printProcess=printProcess,
+                                                                    alpha_=alpha_, yscale=[maxs, mins])
+
+                # Run the model over the validation set 'MC-samples' times and Calculate PIs and metrics
+                if self.method != 'DualAQD' or val:  # DualAQD already performs validation and aggregation in "trainFold"
+                    [mse, PICP, MPIW, _, _, _] = self.calculate_metrics(Xval, Yval, maxs, mins, filepath)
+                print('PERFORMANCE AFTER AGGREGATION:')
+                print("Val MSE: " + str(mse) + " Val PICP: " + str(PICP) + " Val MPIW: " + str(MPIW))
+
+                # Add metrics to the list
+                cvmse.append(mse)
+                cvpicp.append(PICP)
+                cvmpiw.append(MPIW)
+
+                # Reset all weights
+                self.model = self.reset_model()
+            ntrain += 1
+
+        # Save metrics of all folds
+        np.save(folder + '//validation_MSE-' + self.method + "-" + self.dataset, cvmse)
+        np.save(folder + '//validation_MPIW-' + self.method + "-" + self.dataset, cvmpiw)
+        np.save(folder + '//validation_PICP-' + self.method + "-" + self.dataset, cvpicp)
+        if self.dataset == "Synth":
+            np.save(folder + '//validation_DIFFS-' + self.method + "-" + self.dataset, cvdiffs)
+        # Save metrics in a txt file
+        file_name = folder + "//regression_report-" + self.method + "-" + self.dataset + ".txt"
+        with open(file_name, 'w') as x_file:
+            x_file.write("Overall MSE %.6f (+/- %.6f)" % (float(np.mean(cvmse)), float(np.std(cvmse))))
+            x_file.write('\n')
+            x_file.write("Overall PICP %.6f (+/- %.6f)" % (float(np.mean(cvpicp)), float(np.std(cvpicp))))
+            x_file.write('\n')
+            x_file.write("Overall MPIW %.6f (+/- %.6f)" % (float(np.mean(cvmpiw)), float(np.std(cvmpiw))))
+            if self.dataset == "Synth":
+                x_file.write('\n')
+                x_file.write("Overall DIFF %.6f (+/- %.6f)" % (float(np.mean(cvdiffs)), float(np.std(cvdiffs))))
+
+        return cvmse, cvmpiw, cvpicp
+
+    def calculate_metrics(self, Xval, Yval, maxs, mins, filepath=None):
+        """Calculate metrics using MC-Dropout to measure model uncertainty"""
+        startsplit = time.time()
+
+        self.model.loadModel(filepath)  # Load model
+        # Get outputs using trained model
+        yout = self.model.evaluateFoldUncertainty(valxn=Xval, maxs=None, mins=None, batch_size=32, MC_samples=50)
+        yout = np.array(yout)
+        if self.method in ['AQD', 'DualAQD']:
+            # Obtain upper and lower bounds
+            if self.modelType == 'NN':
+                y_u = np.mean(yout[:, 0], axis=1)
+                y_l = np.mean(yout[:, 1], axis=1)
+                # Obtain expected target estimates
+                ypred = np.mean(yout[:, 2], axis=1)
+            else:
+                y_u = np.mean(yout[:, :, :, 0], axis=1)
+                y_l = np.mean(yout[:, :, :, 1], axis=1)
+                # Obtain expected target estimates
+                ypred = np.mean(yout[:, :, :, 2], axis=1)
+            ypred = utils.reverseMinMaxScale(ypred, maxs, mins)
+            y_u = utils.reverseMinMaxScale(y_u, maxs, mins)
+            y_l = utils.reverseMinMaxScale(y_l, maxs, mins)
+        else:
+            # Load validation MSE
+            with open(filepath + '_validationMSE', 'rb') as f:
+                val_MSE = pickle.load(f)
+            # Obtain expected target estimates
+            yout = utils.reverseMinMaxScale(yout, maxs, mins)
+            ypred = np.mean(yout, axis=-1)
+            # Obtain upper and lower bounds
+            model_uncertainty = np.std(yout, axis=-1)
+            y_u = ypred + 1.96 * np.sqrt(model_uncertainty ** 2 + val_MSE)
+            y_l = ypred - 1.96 * np.sqrt(model_uncertainty ** 2 + val_MSE)
+
+        # Reverse normalization process
+        Yval = utils.reverseMinMaxScale(Yval, maxs, mins)
+
+        # Calculate MSE
+        mse = utils.mse(Yval, ypred)
+        # Calculate the coverage vector
+        y_true = torch.from_numpy(Yval).float().to(self.device)
+        y_ut = torch.from_numpy(y_u).float().to(self.device)
+        y_lt = torch.from_numpy(y_l).float().to(self.device)
+        K_U = torch.max(torch.zeros(y_true.size()).to(self.device), torch.sign(y_ut - y_true))
+        K_L = torch.max(torch.zeros(y_true.size()).to(self.device), torch.sign(y_true - y_lt))
+        K = torch.mul(K_U, K_L)
+        # Calculate MPIW
+        MPIW = torch.mean(y_ut - y_lt).item()
+        # Calculate PICP
+        PICP = torch.mean(K).item()
+
+        endsplit = time.time()
+        print("It took " + str(endsplit - startsplit) + " seconds to execute this batch")
+
+        return [mse, PICP, MPIW, ypred, y_u, y_l]
+
+
+if __name__ == '__main__':
+    name = 'Synth'
+    predictor = PIGenerator(dataset=name, method='MCDropout')
+    predictor.train(crossval='10x1', batch_size=32, epochs=1000, printProcess=True, alpha_=0.01, val=False)
+    name = 'FieldA'
+    predictor = PIGenerator(dataset=name, method='MCDropout')
+    predictor.train(crossval='10x1', batch_size=32, epochs=120, printProcess=True, alpha_=0.01, val=False)
+    name = 'FieldB'
+    predictor = PIGenerator(dataset=name, method='MCDropout')
+    predictor.train(crossval='10x1', batch_size=32, epochs=120, printProcess=True, alpha_=0.01, val=False)
diff --git a/models/NNModel.py b/models/NNModel.py
new file mode 100644
index 0000000..546130a
--- /dev/null
+++ b/models/NNModel.py
@@ -0,0 +1,425 @@
+import utils
+import torch
+import pickle
+import random
+import numpy as np
+from tqdm import trange
+from models.network import *
+# import matplotlib.pyplot as plt
+
+np.random.seed(7)  # Initialize seed to get reproducible results
+random.seed(7)
+torch.manual_seed(7)
+torch.cuda.manual_seed(7)
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+
+
+#######################################################################################################################
+# Static functions and Loss functions
+#######################################################################################################################
+
+
+def enable_dropout(model):
+    """ Function to enable the dropout layers during test-time."""
+    for m in model.modules():
+        if m.__class__.__name__.startswith('Dropout'):
+            m.train()
+
+
+def DualAQD_objective(y_pred, y_true, beta_, pe):
+    """Proposed DualAQD loss function,
+    @param y_pred: NN output (y_u, y_l)
+    @param y_true: Ground-truth.
+    @param pe: Point estimate (from the base model).
+    @param beta_: Specify the importance of the width factor."""
+    # Separate upper and lower limits
+    valid = np.where(y_true.cpu().numpy() != 0)
+    y_u = y_pred[:, 0][valid]
+    y_l = y_pred[:, 1][valid]
+    y_o = pe.detach().squeeze(1)[valid]
+    y_true = y_true[valid]
+    # Calculate objectives
+    MPIW_p = torch.mean(torch.abs(y_u - y_true) + torch.abs(y_true - y_l))  # Calculate MPIW_penalty
+    cs = torch.max(torch.abs(y_o - y_true).detach())
+    Constraints = (torch.exp(torch.mean(-y_u + y_true) + cs) +
+                   torch.exp(torch.mean(-y_true + y_l) + cs))
+    # Calculate loss
+    return MPIW_p + Constraints * beta_
+
+
+#######################################################################################################################
+# Class Definitions
+#######################################################################################################################
+
+class NNObject:
+    """Helper class used to store the main information of a NN model."""
+
+    def __init__(self, model, criterion, optimizer):
+        self.network = model
+        self.criterion = criterion
+        self.optimizer = optimizer
+
+
+class NNModel:
+
+    def __init__(self, device, nfeatures, method, modelType='NN', dataset='FieldA'):
+        self.method = method
+        self.modelType = modelType
+        self.device = device
+        self.nfeatures = nfeatures
+        self.basemodel = None  # DualAQD uses a base model trained only for target prediction
+        self.dataset = dataset
+
+        if self.method == 'DualAQD':
+            self.output_size = 2
+        else:  # MC-Dropout
+            self.output_size = 1
+
+        criterion = nn.MSELoss()
+        if modelType == 'NN':
+            if dataset == 'FieldA':  # 4-hidden-layer NN
+                network = NN(input_shape=self.nfeatures, output_size=self.output_size)
+            else:  # 3-hidden-layer NN
+                network = NN2(input_shape=self.nfeatures, output_size=self.output_size)
+        else:
+            network = Hyper3DNetLiteReg(input_shape=self.nfeatures, output_size=5, output_channels=self.output_size)
+        network.to(self.device)
+        # Training parameters
+        optimizer = optim.Adadelta(network.parameters(), lr=0.1)
+
+        self.model = NNObject(network, criterion, optimizer)
+
+    def trainFold(self, Xtrain, Ytrain, Xval, Yval, batch_size, epochs, filepath, printProcess, yscale, alpha_=0.01):
+        if self.method in ['AQD', 'MCDropout']:  # Initialize seed to get reproducible results when using these methods
+            np.random.seed(7)
+            random.seed(7)
+            torch.manual_seed(7)
+            torch.cuda.manual_seed(7)
+            torch.backends.cudnn.deterministic = True
+            torch.backends.cudnn.benchmark = False
+        # Otherwise, QD+ and QD need to generate different NNs each time in order to create a diverse ensemble
+
+        indexes = np.arange(len(Xtrain))  # Prepare list of indexes for shuffling
+        np.random.shuffle(indexes)
+        Xtrain = Xtrain[indexes]
+        Ytrain = Ytrain[indexes]
+
+        indexes = np.arange(len(Xtrain))  # Prepare list of indexes for shuffling
+        np.random.shuffle(indexes)
+        T = np.ceil(1.0 * len(Xtrain) / batch_size).astype(np.int32)  # Compute the number of steps in an epoch
+
+        val_mse = np.infty
+        val_picp = 0
+        val_mpiw = np.infty
+        MPIWtr = []
+        PICPtr = []
+        MSEtr = []
+        MPIW = []
+        PICP = []
+        MSE = []
+        BETA = []
+        widths = [0]
+        picp, picptr, max_picptr, epoch_max_picptr = 0, 0, 0, 0
+        first95 = True  # This is a flag used to check if validation PICP has already reached 95% during the training
+        first100 = False  # This is a flag used to check if training PICP has already reached 100% during the training
+        top = 1
+        tau = 0.95
+        alpha_0 = alpha_
+
+        # If model AQD, start with the pre-trained network
+        if self.method in ['DualAQD']:
+            self.basemodel = NNModel(self.device, self.nfeatures, 'MCDropout', modelType=self.modelType)
+            filepathbase = filepath.replace('DualAQD', 'MCDropout')
+            if 'TuningResults' in filepathbase:
+                filepathbase = filepathbase.replace('TuningResults', 'CVResults')
+            self.basemodel.loadModel(filepathbase)
+            for target_param, param in zip(self.model.network.named_parameters(),
+                                           self.basemodel.model.network.named_parameters()):
+                if 'out' not in target_param[0]:
+                    target_param[1].data.copy_(param[1].data)
+        err_prev, err_new, beta_, beta_prev, d_err = 0, 0, 1, 0, 1
+
+        for epoch in trange(epochs):  # Epoch loop
+            # Batch sorting
+            if epoch > 0 and (self.method in ['DualAQD']):
+                if self.modelType != 'NN':
+                    widths = np.mean(widths, axis=(1, 2))
+                indexes = np.argsort(widths)
+            else:
+                indexes = np.arange(len(Xtrain))  # Prepare list of indexes for shuffling
+            np.random.shuffle(indexes)
+
+            self.model.network.train()  # Sets training mode
+            running_loss = 0.0
+            for step in range(T):  # Batch loop
+                # Generate indexes of the batch
+                inds = indexes[step * batch_size:(step + 1) * batch_size]
+
+                # Get actual batches
+                Xtrainb = torch.from_numpy(Xtrain[inds]).float().to(self.device)
+                Ytrainb = torch.from_numpy(Ytrain[inds]).float().to(self.device)
+
+                # zero the parameter gradients
+                self.model.optimizer.zero_grad()
+
+                # forward + backward + optimize
+                outputs = self.model.network(Xtrainb)
+                if self.method == 'DualAQD':
+                    point_estimates = self.basemodel.model.network(Xtrainb)
+                    loss = DualAQD_objective(outputs, Ytrainb, beta_=beta_, pe=point_estimates)
+                else:
+                    outputs = outputs.squeeze(1)
+                    loss = self.model.criterion(outputs, Ytrainb)
+                loss.backward()
+                self.model.optimizer.step()
+
+                # print statistics
+                running_loss += loss.item()
+                if printProcess and epoch % 10 == 0:
+                    print('[%d, %5d] loss: %.5f' % (epoch + 1, step + 1, loss.item()))
+
+            # Validation step
+            with torch.no_grad():
+                self.model.network.eval()
+                enable_dropout(self.model.network)
+                ypredtr, ypred, petr, pe = 0, 0, 0, 0
+                samples = 5
+                for r in range(samples):
+                    ypredtr += self.model.network(torch.from_numpy(Xtrain).float().to(self.device)).cpu().numpy()
+                    ypred += self.model.network(torch.from_numpy(Xval).float().to(self.device)).cpu().numpy()
+                    if self.method in ['DualAQD']:  # Include base model outputs
+                        self.basemodel.model.network.eval()
+                        petr += self.basemodel.model.network(torch.from_numpy(Xtrain).float().
+                                                             to(self.device)).cpu().numpy()
+                        pe += self.basemodel.model.network(torch.from_numpy(Xval).float().
+                                                           to(self.device)).cpu().numpy()
+                ypredtr /= samples
+                ypred /= samples
+                if self.method in ['DualAQD']:  # Attach base model output to the last column
+                    petr /= samples
+                    pe /= samples
+                    if self.modelType == 'NN':
+                        ypredtr = np.concatenate((ypredtr, petr), axis=1)
+                        ypred = np.concatenate((ypred, pe), axis=1)
+                    else:
+                        temptr = np.zeros((len(Xtrain), 5, 5, 3))
+                        temptr[:, :, :, 0:2], temptr[:, :, :, 2] = ypredtr.transpose((0, 2, 3, 1)), petr
+                        ypredtr = temptr
+                        temp = np.zeros((len(Xval), 5, 5, 3))
+                        temp[:, :, :, 0:2], temp[:, :, :, 2] = ypred.transpose((0, 2, 3, 1)), pe
+                        ypred = temp
+                # Reverse normalization
+                Ytrain_original = utils.reverseMinMaxScale(Ytrain, yscale[0], yscale[1])
+                Yval_original = utils.reverseMinMaxScale(Yval, yscale[0], yscale[1])
+                ypredtr = utils.reverseMinMaxScale(ypredtr, yscale[0], yscale[1])
+                ypred = utils.reverseMinMaxScale(ypred, yscale[0], yscale[1])
+
+                # Calculate MSE
+                if self.method == 'DualAQD':
+                    if self.modelType == 'NN':
+                        msetr = utils.mse(Ytrain_original, ypredtr[:, 2])
+                        mse = utils.mse(Yval_original, ypred[:, 2])
+                    else:
+                        msetr = utils.mse(Ytrain_original, ypredtr[:, :, :, 2])
+                        mse = utils.mse(Yval_original, ypred[:, :, :, 2])
+                else:
+                    if self.modelType == 'NN':
+                        msetr = utils.mse(Ytrain_original, ypredtr[:, 0])
+                        mse = utils.mse(Yval_original, ypred[:, 0])
+                    else:
+                        msetr = utils.mse(Ytrain_original, ypredtr)
+                        mse = utils.mse(Yval_original, ypred)
+                MSEtr.append(msetr)
+                MSE.append(mse)
+                if self.method == 'DualAQD':
+                    # Calculate MPIW and PICP
+                    y_true = torch.from_numpy(Ytrain_original).float().to(self.device)
+                    if self.modelType == 'NN':
+                        y_utr = torch.from_numpy(ypredtr[:, 0]).float().to(self.device)
+                        y_ltr = torch.from_numpy(ypredtr[:, 1]).float().to(self.device)
+                    else:
+                        y_utr = torch.from_numpy(ypredtr[:, :, :, 0]).float().to(self.device)
+                        y_ltr = torch.from_numpy(ypredtr[:, :, :, 1]).float().to(self.device)
+                    K_U = torch.max(torch.zeros(y_true.size()).to(self.device), torch.sign(y_utr - y_true))
+                    K_L = torch.max(torch.zeros(y_true.size()).to(self.device), torch.sign(y_true - y_ltr))
+                    Ktr = torch.mul(K_U, K_L)
+                    picptr = torch.mean(Ktr).item()
+                    y_true = torch.from_numpy(Yval_original).float().to(self.device)
+                    if self.modelType == 'NN':
+                        y_u = torch.from_numpy(ypred[:, 0]).float().to(self.device)
+                        y_l = torch.from_numpy(ypred[:, 1]).float().to(self.device)
+                    else:
+                        y_u = torch.from_numpy(ypred[:, :, :, 0]).float().to(self.device)
+                        y_l = torch.from_numpy(ypred[:, :, :, 1]).float().to(self.device)
+                    K_U = torch.max(torch.zeros(y_true.size()).to(self.device), torch.sign(y_u - y_true))
+                    K_L = torch.max(torch.zeros(y_true.size()).to(self.device), torch.sign(y_true - y_l))
+                    K = torch.mul(K_U, K_L)
+                    # Update curves
+                    MPIWtr.append((torch.sum(torch.mul((y_utr - y_ltr), Ktr)) / (torch.sum(Ktr) + 0.0001)).item())
+                    PICPtr.append(picptr)
+                    width = (torch.sum(torch.mul((y_u - y_l), K)) / (torch.sum(K) + 0.0001)).item()
+                    picp = torch.mean(K).item()
+                    MPIW.append(width)
+                    PICP.append(picp)
+                    # Get a vector of all the PI widths in the training set
+                    widths = (y_utr - y_ltr).cpu().numpy()
+
+            # Save model if PICP increases
+            if self.method == 'DualAQD':
+                # Criteria 1: If <95, choose max picp, if picp>95, choose any picp if width<minimum width
+                if (((val_picp == picp < tau and width < val_mpiw) or (val_picp < picp < tau)) and first95) or \
+                        (picp >= tau and first95) or \
+                        (picp >= tau and width < val_mpiw and not first95):  # and val_std < std
+                    if picp >= tau:
+                        first95 = False
+                    val_mse = mse
+                    val_picp = picp
+                    val_mpiw = width
+                    if filepath is not None:
+                        torch.save(self.model.network.state_dict(), filepath)
+            else:  # Save model if MSE decreases
+                if mse < val_mse:
+                    val_mse = mse
+                    if filepath is not None:
+                        torch.save(self.model.network.state_dict(), filepath)
+
+            # Check if picp has reached convergence
+            if picptr > max_picptr:
+                max_picptr = picptr
+                epoch_max_picptr = epoch
+            else:
+                if epoch == epoch_max_picptr + 50 and \
+                        picptr <= max_picptr:  # If 30 epochs have passed without increasing PICP
+                    first100 = True
+                    top = tau
+                    alpha_0 = alpha_ / 2
+
+            # Beta hyperparameter
+            if picptr >= 0.999 and not first100:
+                first100 = True
+                top = tau
+                alpha_0 = alpha_ / 2
+            err_new = top - picptr
+            beta_ = beta_ + alpha_0 * err_new
+            # Update parameters
+            BETA.append(beta_)
+
+            # Print every 10 epochs
+            if printProcess and epoch % 10 == 0:
+                if self.method == 'MCDropout':
+                    print('VALIDATION: Training_MSE: %.5f. MSE val: %.5f, Best_MSE: %.5f' % (msetr, mse, val_mse))
+                else:
+                    print('VALIDATION: Training_MSE: %.5f. Best_MSEval: %.5f. MSE val: %.5f. PICP val: %.5f. '
+                          'MPIW val: %.5f'
+                          % (msetr, val_mse, mse, picp, width))
+                    print(val_picp)
+                    print(val_mpiw)
+                    print(picptr)
+                    print(beta_)
+                    print(first100)
+
+        # Save model
+        if filepath is not None:
+            with open(filepath + '_validationMSE', 'wb') as fil:
+                pickle.dump(val_mse, fil)
+            # Save history
+            np.save(filepath + '_historyMSEtr', MSEtr)
+            np.save(filepath + '_historyMSE', MSE)
+            if 'QD' in self.method:  # Average upper and lower limit to obtain expected output
+                np.save(filepath + '_historyMPIWtr', MPIWtr)
+                np.save(filepath + '_historyPICPtr', PICPtr)
+                np.save(filepath + '_historyMPIW', MPIW)
+                np.save(filepath + '_historyPICP', PICP)
+
+        return MPIW, PICP, MSE, val_mse, val_picp, val_mpiw
+
+    def evaluateFold(self, valxn, maxs, mins, batch_size):
+        """Retrieve point predictions."""
+        if maxs is not None and mins is not None:
+            valxn = utils.reverseMinMaxScale(valxn, maxs, mins)
+
+        ypred = []
+        with torch.no_grad():
+            self.model.network.eval()
+            Teva = np.ceil(1.0 * len(valxn) / batch_size).astype(np.int32)
+            indtest = np.arange(len(valxn))
+            for b in range(Teva):
+                inds = indtest[b * batch_size:(b + 1) * batch_size]
+                if self.method == 'DualAQD':
+                    ypred_batch = self.basemodel.model.network(torch.from_numpy(valxn[inds]).float().to(self.device))
+                else:
+                    ypred_batch = self.model.network(torch.from_numpy(valxn[inds]).float().to(self.device))
+                ypred = ypred + (ypred_batch.cpu().numpy()).tolist()
+
+        return ypred
+
+    def evaluateFoldUncertainty(self, valxn, maxs, mins, batch_size, MC_samples):
+        """Retrieve point predictions and PIs"""
+        np.random.seed(7)  # Initialize seed to get reproducible results
+        random.seed(7)
+        torch.manual_seed(7)
+        torch.cuda.manual_seed(7)
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+        if maxs is not None and mins is not None:
+            valxn = utils.reverseMinMaxScale(valxn, maxs, mins)
+
+        with torch.no_grad():
+            if self.modelType == 'NN':
+                preds_MC = np.zeros((len(valxn), MC_samples))
+            else:
+                preds_MC = np.zeros((len(valxn), 5, 5, MC_samples))
+            if self.method == "DualAQD":
+                if self.modelType == 'NN':
+                    preds_MC = np.zeros((len(valxn), 3, MC_samples))
+                else:
+                    preds_MC = np.zeros((len(valxn), 3, 5, 5, MC_samples))
+            for it in range(0, MC_samples):  # Test the model 'MC_samples' times
+                ypred = []
+                self.model.network.eval()
+                # enable_dropout(self.model.network)  # Set Dropout layers to test mode
+                Teva = np.ceil(1.0 * len(valxn) / batch_size).astype(np.int32)  # Number of batches
+                indtest = np.arange(len(valxn))
+                for b in range(Teva):
+                    inds = indtest[b * batch_size:(b + 1) * batch_size]
+                    ypred_batch = self.model.network(torch.from_numpy(valxn[inds]).float().to(self.device))
+                    if self.method == "DualAQD":
+                        self.basemodel.model.network.eval()
+                        enable_dropout(self.basemodel.model.network)
+                        ypred_batch = ypred_batch.cpu().numpy()
+                        ypred_batchtmp = np.zeros((ypred_batch.shape[0], 3))
+                        pe_batch = self.basemodel.model.network(torch.from_numpy(valxn[inds]).float().to(self.device))
+                        pe_batch = pe_batch.cpu().numpy()
+                        if self.modelType == 'NN':
+                            ypred_batchtmp[:, :2] = ypred_batch
+                            ypred_batchtmp[:, 2] = pe_batch.squeeze(1)
+                        else:
+                            ypred_batchtmp[:, :2, :, :] = ypred_batch
+                            ypred_batchtmp[:, 2, :, :] = pe_batch.squeeze(1)
+                        ypred_batch = ypred_batchtmp
+                    else:
+                        ypred_batch = ypred_batch.squeeze(1)
+                    ypred = ypred + ypred_batch.tolist()
+
+                if self.method == "DualAQD":
+                    if self.modelType == 'NN':
+                        preds_MC[:, :, it] = np.array(ypred)
+                    else:
+                        preds_MC[:, :, :, :, it] = np.array(ypred)
+                else:
+                    if self.modelType == 'NN':
+                        preds_MC[:, it] = np.array(ypred)
+                    else:
+                        preds_MC[:, :, :, it] = np.array(ypred)
+        return preds_MC
+
+    def loadModel(self, path):
+        self.model.network.load_state_dict(torch.load(path, map_location=self.device))
+
+        if self.method == 'DualAQD':
+            self.basemodel = NNModel(self.device, self.nfeatures, 'MCDropout', dataset=self.dataset, modelType=self.modelType)
+            filepathbase = path.replace('DualAQD', 'MCDropout')
+            if 'TuningResults' in filepathbase:
+                filepathbase = filepathbase.replace('TuningResults', 'CVResults')
+            self.basemodel.loadModel(filepathbase)
diff --git a/models/network.py b/models/network.py
new file mode 100644
index 0000000..c5c42eb
--- /dev/null
+++ b/models/network.py
@@ -0,0 +1,188 @@
+from abc import ABC
+import torch.nn as nn
+from torch import reshape, cat, Tensor, matmul, no_grad, optim, rand, index_select
+
+
+class NN(nn.Module, ABC):
+    """Defines NN architecture for yield prediction"""
+
+    def __init__(self, input_shape=500, output_size=1):
+        super(NN, self).__init__()
+        self.hidden_layer1 = nn.Sequential(
+            nn.Linear(in_features=input_shape, out_features=100), nn.ReLU())
+        self.drop1 = nn.Dropout(p=0.1)
+        self.hidden_layer2 = nn.Sequential(
+            nn.Linear(in_features=100, out_features=500), nn.ReLU())
+        self.drop2 = nn.Dropout(p=0.1)
+        self.hidden_layer3 = nn.Sequential(
+            nn.Linear(in_features=500, out_features=100), nn.ReLU())
+        self.hidden_layer4 = nn.Sequential(
+            nn.Linear(in_features=100, out_features=50), nn.ReLU())
+
+        # Number of outputs depends on the method
+        self.out = nn.Linear(50, output_size)
+
+    def forward(self, x):
+        x = self.hidden_layer1(x)
+        x = self.drop1(x)
+        x = self.hidden_layer2(x)
+        x = self.drop2(x)
+        x = self.hidden_layer3(x)
+        x = self.hidden_layer4(x)
+        return self.out(x)
+
+
+class NNcounterfact(nn.Module, ABC):
+    """Defines NN architecture for yield prediction"""
+
+    def __init__(self, input_shape=500, output_size=1):
+        super(NNcounterfact, self).__init__()
+        self.hidden_layer1 = nn.Sequential(
+            nn.Linear(in_features=input_shape, out_features=100), nn.ReLU())
+        self.drop1 = nn.Dropout(p=0.1)
+        self.hidden_layer2 = nn.Sequential(
+            nn.Linear(in_features=100, out_features=500), nn.ReLU())
+        self.drop2 = nn.Dropout(p=0.1)
+        self.hidden_layer3 = nn.Sequential(
+            nn.Linear(in_features=500, out_features=100), nn.ReLU())
+        self.hidden_layer4 = nn.Sequential(
+            nn.Linear(in_features=100, out_features=50), nn.ReLU())
+
+        # Number of outputs depends on the method
+        self.out = nn.Linear(50, output_size)
+
+        # Number of secondary outputs depends on the dimensionality of the input minus 1
+        self.out2 = nn.Linear(50, input_shape - 1)
+
+    def forward(self, x):
+        x = self.hidden_layer1(x)
+        x = self.drop1(x)
+        x = self.hidden_layer2(x)
+        x = self.drop2(x)
+        x = self.hidden_layer3(x)
+        x = self.hidden_layer4(x)
+        return [self.out(x), self.out2(x)]
+
+
+class NN2(nn.Module, ABC):
+    """Defines NN architecture for the other datasets"""
+
+    def __init__(self, input_shape=500, output_size=1):
+        super(NN2, self).__init__()
+        self.hidden_layer1 = nn.Sequential(
+            nn.Linear(in_features=input_shape, out_features=100), nn.ReLU())
+        self.drop1 = nn.Dropout(p=0.1)
+        self.hidden_layer2 = nn.Sequential(
+            nn.Linear(in_features=100, out_features=100), nn.Tanh())
+        self.drop2 = nn.Dropout(p=0.1)
+
+        # Number of outputs depends on the method
+        self.out = nn.Linear(100, output_size)
+
+    def forward(self, x):
+        x = self.hidden_layer1(x)
+        x = self.drop1(x)
+        x = self.hidden_layer2(x)
+        x = self.drop2(x)
+        return self.out(x)
+
+
+class Hyper3DNetLiteReg(nn.Module, ABC):
+    """Our proposed 3D-2D CNN."""
+
+    def __init__(self, input_shape=(1, 15, 5, 5), output_size=5, output_channels=1):
+        super(Hyper3DNetLiteReg, self).__init__()
+        # Set stride
+        stride = 1
+        # If the size of the output patch is less than the input size, don't apply padding at the end
+        if output_size < input_shape[2]:
+            padding = 0
+        else:
+            padding = 1
+
+        self.input_shape = input_shape
+        self.output_size = output_size
+        self.output_channels = output_channels
+        nfilters = 32
+
+        self.conv_layer1 = nn.Sequential(
+            nn.Conv3d(in_channels=input_shape[0], out_channels=nfilters, kernel_size=3, padding=1),
+            nn.ReLU(), nn.BatchNorm3d(nfilters))
+        self.conv_layer2 = nn.Sequential(
+            nn.Conv3d(in_channels=nfilters, out_channels=nfilters, kernel_size=3, padding=1),
+            nn.ReLU(), nn.BatchNorm3d(nfilters))
+        self.conv_layer3 = nn.Sequential(
+            nn.Conv3d(in_channels=nfilters * 2, out_channels=nfilters, kernel_size=3, padding=1),
+            nn.ReLU(), nn.BatchNorm3d(nfilters))
+        # self.drop0 = nn.Dropout(p=0.5)
+        self.conv_layer4 = nn.Sequential(
+            nn.Conv3d(in_channels=nfilters * 3, out_channels=nfilters, kernel_size=3, padding=1),
+            nn.ReLU(), nn.BatchNorm3d(nfilters))
+
+        self.drop = nn.Dropout(p=0.1)
+
+        self.sepconv1 = nn.Sequential(
+            nn.Conv2d(in_channels=nfilters * 4 * input_shape[1], out_channels=nfilters * 4 * input_shape[1],
+                      kernel_size=3, padding=1, groups=nfilters * 4 * input_shape[1]), nn.ReLU(),
+            nn.Conv2d(in_channels=nfilters * 4 * input_shape[1], out_channels=512,
+                      kernel_size=1, padding=0), nn.ReLU(), nn.BatchNorm2d(512))
+        self.sepconv2 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=512,
+                                                kernel_size=3, padding=1, stride=stride, groups=512), nn.ReLU(),
+                                      nn.Conv2d(in_channels=512, out_channels=320,
+                                                kernel_size=1, padding=0), nn.ReLU(), nn.BatchNorm2d(320))
+        self.drop2 = nn.Dropout(p=0.1)
+        self.sepconv3 = nn.Sequential(nn.Conv2d(in_channels=320, out_channels=320,
+                                                kernel_size=3, padding=1, stride=stride, groups=320), nn.ReLU(),
+                                      nn.Conv2d(in_channels=320, out_channels=256,
+                                                kernel_size=1, padding=0), nn.ReLU(), nn.BatchNorm2d(256))
+        self.drop3 = nn.Dropout(p=0.1)
+        self.sepconv4 = nn.Sequential(nn.Conv2d(in_channels=256, out_channels=256,
+                                                kernel_size=3, padding=1, stride=stride, groups=256), nn.ReLU(),
+                                      nn.Conv2d(in_channels=256, out_channels=128,
+                                                kernel_size=1, padding=0), nn.ReLU(), nn.BatchNorm2d(128))
+        self.sepconv5 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=32,
+                                                kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32))
+
+        # This layer is used in case the outputSize is 1
+        if output_size == 1:
+            self.out = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=1,
+                                               kernel_size=3, padding=padding), nn.ReLU())
+            self.fc = nn.Linear(9, output_channels)
+        else:
+            self.out = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=output_channels,
+                                               kernel_size=3, padding=padding))
+
+    def forward(self, x):
+        # print(device)
+        # 3D Feature extractor
+        x = self.conv_layer1(x)
+        x2 = self.conv_layer2(x)
+        x = cat((x, x2), 1)
+        x2 = self.conv_layer3(x)
+        x = cat((x, x2), 1)
+        # x = self.drop0(x)
+        x2 = self.conv_layer4(x)
+        x = cat((x, x2), 1)
+        # Reshape 3D-2D
+        x = reshape(x, (x.shape[0], x.shape[1] * x.shape[2], x.shape[3], x.shape[4]))
+        x = self.drop(x)
+        # 2D Spatial encoder
+        x = self.sepconv1(x)
+        x = self.sepconv2(x)
+        x = self.drop2(x)
+        x = self.sepconv3(x)
+        x = self.drop3(x)
+        x = self.sepconv4(x)
+        x = self.sepconv5(x)
+        # Final output
+        x = self.out(x)
+
+        # Flatten and apply the last fc layer if the output is just a number
+        if self.output_size == 1:
+            x = reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))
+            x = self.fc(x)
+        else:
+            # Reshape 2D
+            if self.output_channels == 1:
+                x = reshape(x, (x.shape[0], x.shape[1] * x.shape[2], x.shape[3]))
+        return x
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..144cdec
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,146 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def mse(imageA, imageB):
+    """Calculate the 'Mean Squared Error' between the two images."""
+    return np.mean((imageA - imageB) ** 2)
+
+
+def normalize(trainx):
+    """Normalize and returns the calculated means and stds for each feature"""
+    trainxn = trainx.copy()
+    if 1 < trainxn.ndim < 4:
+        means = np.zeros((trainx.shape[1], 1))
+        stds = np.zeros((trainx.shape[1], 1))
+        for n in range(trainx.shape[1]):
+            means[n, ] = np.mean(trainxn[:, n])
+            stds[n, ] = np.std(trainxn[:, n])
+            trainxn[:, n] = (trainxn[:, n] - means[n, ]) / (stds[n, ])
+    elif trainxn.ndim > 4:
+        means = np.zeros((trainx.shape[2], 1))
+        stds = np.zeros((trainx.shape[2], 1))
+        for n in range(trainx.shape[2]):
+            means[n, ] = np.mean(trainxn[:, :, n, :, :])
+            stds[n, ] = np.std(trainxn[:, :, n, :, :])
+            trainxn[:, :, n, :, :] = (trainxn[:, :, n, :, :] - means[n, ]) / (stds[n, ])
+    else:
+        means = np.mean(trainxn)
+        stds = np.std(trainxn)
+        trainxn = (trainxn - means) / stds
+    return trainxn, means, stds
+
+
+def applynormalize(testx, means, stds):
+    """Apply normalization based on previous calculated means and stds"""
+    testxn = testx.copy()
+    if 1 < testxn.ndim < 4:
+        for n in range(testx.shape[1]):
+            testxn[:, n] = (testxn[:, n] - means[n, ]) / (stds[n, ])
+    elif testxn.ndim >= 4:
+        for n in range(testx.shape[2]):
+            testxn[:, :, n, :, :] = (testxn[:, :, n, :, :] - means[n, ]) / (stds[n, ])
+    else:
+        testxn = (testxn - means) / stds
+    return testxn
+
+
+def reversenormalize(testx, means, stds):
+    """Reverse normalization based on previous calculated means and stds"""
+    testxn = testx.copy()
+    if 1 < testxn.ndim < 4:
+        for n in range(testx.shape[1]):
+            testxn[:, n] = (testxn[:, n] * stds) + means
+    elif testxn.ndim > 4:
+        for n in range(testx.shape[2]):
+            testxn[:, :, n, :, :] = (testxn[:, :, n, :, :] * stds[n, ]) + means[n, ]
+    else:
+        testxn = (testxn * stds) + means
+
+    return testxn
+
+
+def minMaxScale(trainx):
+    """Normalize and returns the calculated max and min of the output"""
+    trainxn = trainx.copy()
+    maxs = np.max(trainxn)
+    mins = np.min(trainxn)
+    trainxn = (trainxn - mins) / (maxs - mins) * 10
+    return trainxn, maxs, mins
+
+
+def applyMinMaxScale(testx, maxs, mins):
+    """Apply normalization based on previous calculated means and stds"""
+    testxn = testx.copy()
+    testxn = (testxn - mins) / (maxs - mins) * 10
+    return testxn
+
+
+def reverseMinMaxScale(testx, maxs, mins):
+    """Apply normalization based on previous calculated means and stds"""
+    testxn = testx.copy()
+    if testxn.ndim > 1:
+        for n in range(testx.shape[1]):
+            testxn[:, n] = (testxn[:, n] * (maxs - mins) / 10) + mins
+    else:
+        testxn = (testxn * (maxs - mins) / 10) + mins
+
+    return testxn
+
+
+def create_synth_data(n=1000, plot=False):
+    """Create a synthetic sinusoidal dataset with varying PI width"""
+    np.random.seed(7)
+    X = np.linspace(-5, 5, num=n)
+    randn = np.random.normal(size=n)
+    gauss = (2 + 2 * np.cos(1.2 * X))
+    noise = gauss * randn
+    orig = 10 + 5 * np.cos(X + 2)
+    Y = orig + noise
+    P1 = orig + 1.96 * gauss
+    P2 = orig - 1.96 * gauss
+    if plot:
+        plt.figure(figsize=(9.97, 7.66))
+        plt.fill_between(X, P1, P2, color='gray', alpha=0.5, linewidth=0, label='Ideal 95% PIs')
+        plt.scatter(X, Y, label='Data with noise')
+        plt.plot(X, orig, 'r', label='True signal')
+        plt.legend()
+    return X, Y, P1, P2
+
+
+def gower(x1, x2, types, ranges):
+    """Calculate Gower distance"""
+    dist = []
+    for j in range(len(types)):
+        if (types[j] == 'categorical') or (types[j] == 'binary'):
+            dist.append(int(x1[j] != x2[j]))
+        else:
+            if x1.ndim <= 2:
+                dist.append((np.abs(x1[j] - x2[j])) / ranges[j])
+            else:
+                dist.append((np.abs(x1[0, j, 2, 2] - x2[0, j, 2, 2])) / ranges[j])
+    return np.array(dist)
+
+
+def plot_numpy_matrix(cm, cmap=plt.cm.Reds):
+    """
+    This function prints and plots a numpy array as an image.
+    Normalization can be applied by setting `normalize=True`.
+    """
+    label_font = {'size': '15'}
+    fig, ax = plt.subplots(figsize=(2.5, 3.4))
+    ax.imshow(cm, interpolation='nearest', cmap=cmap, aspect='auto', vmin=0, vmax=100)
+    plt.xticks(np.arange(6), ['S\n$r_2$', 'TPI\n$r_3$', 'A\n$r_4$', 'P\n$r_5$', 'VH\n$r_6$', 'VV\n$r_7$'], fontsize=13)
+    ax.xaxis.tick_top()
+    plt.yticks(np.arange(10), np.arange(1, 11), fontsize=11)
+
+    for i in range(cm.shape[0]):
+        for j in range(cm.shape[1]):
+            plt.text(j, i, '{0:.0f}'.format(cm[i, j]),
+                         horizontalalignment="center",
+                         verticalalignment="center", fontsize=11,
+                         color="white" if cm[i, j] > 50 else "black",fontweight='bold')
+
+    plt.tight_layout()
+    plt.ylabel('CV Iteration', fontdict=label_font)
+    plt.xlabel('$ϵ = 0.6$', fontdict=label_font)