Added TSNE Plot

app.py

@@ -7,6 +7,7 @@ import streamlit as st
 
 from Data_Generation.Dataset_Generation_Functions import make_boxes
 from Data_Generation.Piecewise_Box_Functions import basic_box_array, forward_slash_array, combine_arrays, add_thickness
+from Data_Plotting.Plot_TSNE import TSNE_reduction
 ########################################################################################################################
 # User Inputs
 image_size = st.slider('Select a value for the image size', min_value=9, max_value=16)
@@ -73,6 +74,21 @@ if st.button('Generate Dataset'):  # Generate the dataset
     box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness\
         = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3], list(zip(*boxes))[4], list(zip(*boxes))[5], list(zip(*boxes))[6]
 
+    # Plot TSNE of the data
+    # define a function to flatten a 2D array
+    def flatten_array(array):
+        return array.flatten()
+
+    # apply the flatten_array function to each array in the list and create a list of flattened arrays
+    flattened_arrays = [flatten_array(a) for a in box_arrays]
+
+    # Perform the TSNE Reduction
+    x, y, title, embedding = TSNE_reduction(flattened_arrays)
+
+    plt.scatter(x, y)
+    plt.title(title)
+    plt.show()
+
     class NumpyArrayEncoder(JSONEncoder):
         def default(self, obj):
             if isinstance(obj, np.ndarray):