Finalizing notes and type hints

Data_Plotting/Plot_TSNE.py

@@ -6,15 +6,10 @@ import numpy as np
 # Latent Feature Cluster for Training Data using T-SNE
 def TSNE_reduction(latent_points: np.ndarray, perplexity=30, learning_rate=20):
     """
-    :param latent_points: [ndarray] - an array of arrays that define the points of an object in the latent space
-    :param perplexity: [int] - default perplexity = 30 " Perplexity balances the attention t-SNE gives to local and
-    global aspects of the data. It is roughly a guess of the number of close neighbors each point has...
-    a denser dataset ... requires higher perplexity value" Recommended: Perplexity(5-50)
-    :param learning_rate: [int] - default learning rate = 200 "If the learning rate is too high, the data may look
-    like a ‘ball’ with any point approximately equidistant from its nearest neighbours.
-    If the learning rate is too low, most points may look compressed in a dense cloud with few outliers."
-    Recommended: learning_rate(10-1000)
-    :return: [tuple] - the output is the x and y coordinates for the reduced latent space, a title, and an embedding
+    :param latent_points: [ndarray] - an array of arrays that define the points of multiple objects in the latent space
+    :param perplexity: [int] - default perplexity = 30 " Perplexity balances the attention t-SNE gives to local and global aspects of the data. It is roughly a guess of the number of close neighbors each point has... a denser dataset ... requires higher perplexity value" Recommended: Perplexity(5-50)
+    :param learning_rate: [int] - default learning rate = 200 "If the learning rate is too high, the data may look like a ‘ball’ with any point approximately equidistant from its nearest neighbours. If the learning rate is too low, most points may look compressed in a dense cloud with few outliers." Recommended: learning_rate(10-1000)
+    :return: [tuple] - the output is the x and y coordinates for the reduced latent space, a title, and a TSNE embedding
     """
     model = TSNE(n_components=2, random_state=0, perplexity=perplexity,
                  learning_rate=learning_rate)
@@ -31,8 +26,14 @@ def TSNE_reduction(latent_points: np.ndarray, perplexity=30, learning_rate=20):
 
 
 def plot_dimensionality_reduction(x: list, y: list, label_set: list, title: str):
+    """
+    :param x: [list] - the first set of coordinates for each latent point
+    :param y: [list] - the second set of coordinates for each latent point
+    :param label_set: [list] - a set of values that define the color of each point based on an additional quantitative attribute.
+    :return: matplotlib figure - the output is a matplotlib figure that displays all the points in a 2-dimensional latent space, based on the labels provided.
+    """
     plt.title(title)
-    # Color points based on their density
+    # Color points based on a continuous label
     if label_set[0].dtype == float:
         plt.scatter(x, y, c=label_set)
         cbar = plt.colorbar()

app.py

@@ -89,9 +89,7 @@ if st.button('Generate Dataset'):  # Generate the dataset
     plt.figure(3)
     # set the color values for the plot
     plot_dimensionality_reduction(x, y, avg_density, title)
-
-    # plt.scatter(x, y)
-    plt.title(title)
+    # plt.title(title)
     plt.figure(3)
     st.pyplot(plt.figure(3))