Delete uilit.py

uilit.py

@@ -1,135 +0,0 @@
-from init import *
-import numpy as np 
-import pandas as pd
-import matplotlib.pyplot as plt
-import os
-from PIL import Image
-
-import matplotlib.animation as animation
-from matplotlib.animation import FuncAnimation
-from IPython.display import clear_output
-from autograd.core.engine import Value
-from sklearn.datasets import make_moons, make_blobs
-import plotly.graph_objects as go
-import plotly.io as pio
-import imageio
-
-clear_output()
-
-# Iterate through the first 6 images
-
-def extract_path_df(path_dir, index_show):
-    path_file = []
-
-    for filesname in os.listdir(path_dir):
-        path_file.append(os.path.join(path_dir,filesname))
-
-    for data_df in range(0,len(path_file)):
-        data_frame = pd.read_csv(path_file[data_df])
-        show_df = data_frame.head(index_show)
-    return path_file , f"dataframe: {show_df}"
-
-def loading_df_to_numpy(path_file):
-    data_df = pd.read_csv(path_file)
-    data = np.array(data_df) 
-    m, n = data.shape
-    data_train = data[1000:m].T
-    Y_train = data_train[0][40900:]
-    X_train = data_train[1:n][:, 40900:]
-
-
-    # Manually split the data into training and testing sets
-    split_index = int(X_train.shape[0] * 0.8)  # 80% for training, 20% for testing
-    X_train_split = X_train[:, :10]
-    Y_train_split = Y_train[:10]
-    X_test_split = X_train[:,:5]
-    Y_test_split = Y_train[:5]
-
-    return X_train_split, X_test_split, Y_train_split, Y_test_split
-
-def initialize_data(n_samples: int, noise: float):
-    input_data, Target = make_moons(n_samples=n_samples, noise=noise)
-
-    Target = Target * 2 - 1  # make y be -1 or 14
-    # fig.close()
-    return input_data, Target
-
-
-def plot_sample(DATA_TRAIN,DATA_LABEL):
-    num_images = min(6, DATA_TRAIN.shape[1])
-    fig, axs = plt.subplots(2, 3, figsize=(10, 7))
-
-    for i in range(num_images):
-        label = DATA_LABEL
-        image = DATA_TRAIN[:, i]
-        current_image = image.reshape(28, 28) * 255
-
-        # Determine the subplot coordinates
-        row = i // 3
-        col = i % 3
-
-    # Plot the image in the corresponding subplot
-        axs[row, col].imshow(current_image, cmap='gray')
-        axs[row, col].set_title("Label: {}".format(label))
-        axs[row, col].axis('off')
-
-    plt.tight_layout()
-    plt.savefig("sample.png")
-
-def copy(model):
-  model = SparseMLP(nin=2, nouts=[16, 16, 1], sparsities=[0.,0.9,0.8])
-  model.parameters()
-  return model
-
-def Zvals(model , X_train):
-  global X
-  h = 0.25
-  x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
-  y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
-  xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
-                      np.arange(y_min, y_max, h))
-  Xmesh = np.c_[xx.ravel(), yy.ravel()]
-  inputs = [list(map(Value, xrow)) for xrow in Xmesh]
-  scores = list(map(model, inputs))
-  Z = np.array([s.data > 0 for s in scores])
-  Z = Z.reshape(xx.shape)
-  return Z
-
-
-def dboundary(model):
-  global X
-  global Y
-  h = 0.25
-  x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
-  y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
-  xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
-                      np.arange(y_min, y_max, h))
-  Xmesh = np.c_[xx.ravel(), yy.ravel()]
-  fig, ax = plt.subplots(figsize=(8,8))
-  Z = Zvals(model)
-  ln = ax.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)
-  ax.scatter(X[:, 0], X[:, 1], c=Y, s=40, cmap=plt.cm.Spectral)
-  ax.set_xlim(xx.min(), xx.max())
-  ax.set_ylim(yy.min(), yy.max())
-  return  fig,ax,ln
-
-def graph_trace(Path, nframes, interval):
-    animation_frames = []
-    # Load the first image to get its dimensions and color mode
-    first_frame_path = f"assets/{Path}_0.png"
-    first_image = Image.open(first_frame_path)
-    width, height = first_image.size
-    color_mode = first_image.mode
-
-    # Resize and convert all the images to the same dimensions and color mode
-    resized_width = 900  # Set your desired width
-    resized_height = 700  # Set your desired height
-
-    for i in range(nframes):
-        frame_path = f"assets/{Path}_{i}.png"
-        image = Image.open(frame_path)
-        resized_image = image.resize((resized_width, resized_height)).convert(color_mode)
-        animation_frames.append(resized_image)
-
-    animation_path = "out/Graph.mp4"
-    imageio.mimsave(animation_path, animation_frames, format="mp4")