Resolve "Update EDA"

Closes #13 (closed)

EDA.py

@@ -23,7 +23,7 @@ def find_mean_img(full_mat):
     # calculate the average
         mean_img = np.mean(full_mat[mat], axis = 0)
     # reshape it back to a matrix
-        mean_img = mean_img.reshape((200, 150))
+        mean_img = mean_img.reshape((170, 120))
         ax = fig.add_subplot(rows, cols,i+1)
         ax.imshow(mean_img, vmin=0, vmax=255, cmap='Greys_r')
         ax.set_title('Average ' + mat)
@@ -31,7 +31,7 @@ def find_mean_img(full_mat):
 
     plt.tight_layout()
     
-def plot_pca(pca, title, size = (200, 150)):
+def plot_pca(pca, title, size = (170, 120)):
     """Plots each decomposed PCA image and labels the amount of variability for each image.
 
     Args:
@@ -57,7 +57,7 @@ def plot_pca(pca, title, size = (200, 150)):
     plt.show()
 
 
-def eigenimages(full_mat,n_comp = 0.7, size = (200, 150)):
+def eigenimages(full_mat,n_comp = 0.7, size = (170, 120)):
     """Creates creates and fits a PCA estimator from sklearn.
 
     Args:
@@ -69,6 +69,7 @@ def eigenimages(full_mat,n_comp = 0.7, size = (200, 150)):
         sklearn PCA object: Fitted PCA model.
     """
     # fit PCA to describe n_comp * variability in the class
+    print(full_mat, n_comp)
     pca = PCA(n_components = n_comp, whiten = True)
     pca.fit(full_mat)