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diff --git a/hw1/T1_P2.py b/hw1/T1_P2.py
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-#####################
-# CS 181, Spring 2021
-# Homework 1, Problem 2
-# Start Code
-##################
-
-import math
-import matplotlib.cm as cm
-
-from math import exp
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.colors as c
-
-# Read from file and extract X and y
-df = pd.read_csv('data/p2.csv')
-
-X_df = df[['x1', 'x2']]
-y_df = df['y']
-
-X = X_df.values
-y = y_df.values
-
-print("y is:")
-print(y)
-
-def predict_kernel(alpha=0.1):
-    """Returns predictions using kernel-based predictor with the specified alpha."""
-    # TODO: your code here
-    return y_df
-
-def predict_knn(k=1):
-    """Returns predictions using KNN predictor with the specified k."""
-    # TODO: your code here
-    return y_df
-
-def plot_kernel_preds(alpha):
-    title = 'Kernel Predictions with alpha = ' + str(alpha)
-    plt.figure()
-    plt.title(title)
-    plt.xlabel('x1')
-    plt.ylabel('x2')
-    plt.xlim((0, 1))
-    plt.ylim((0, 1))
-
-    plt.xticks(np.arange(0, 1, 0.1))
-    plt.yticks(np.arange(0, 1, 0.1))
-    y_pred = predict_kernel(alpha)
-    print(y_pred)
-    print('L2: ' + str(sum((y - y_pred) ** 2)))
-    norm = c.Normalize(vmin=0.,vmax=1.)
-    plt.scatter(df['x1'], df['x2'], c=y_pred, cmap='gray', vmin=0, vmax = 1, edgecolors='b')
-    for x_1, x_2, y_ in zip(df['x1'].values, df['x2'].values, y_pred):
-        plt.annotate(str(round(y_, 2)),
-                     (x_1, x_2), 
-                     textcoords='offset points',
-                     xytext=(0,5),
-                     ha='center') 
-
-    # Saving the image to a file, and showing it as well
-    plt.savefig('alpha' + str(alpha) + '.png')
-    plt.show()
-
-def plot_knn_preds(k):
-    title = 'KNN Predictions with k = ' + str(k)
-    plt.figure()
-    plt.title(title)
-    plt.xlabel('x1')
-    plt.ylabel('x2')
-    plt.xlim((0, 1))
-    plt.ylim((0, 1))
-
-    plt.xticks(np.arange(0, 1, 0.1))
-    plt.yticks(np.arange(0, 1, 0.1))
-    y_pred = predict_knn(k)
-    print(y_pred)
-    print('L2: ' + str(sum((y - y_pred) ** 2)))
-    norm = c.Normalize(vmin=0.,vmax=1.)
-    plt.scatter(df['x1'], df['x2'], c=y_pred, cmap='gray', vmin=0, vmax = 1, edgecolors='b')
-    for x_1, x_2, y_ in zip(df['x1'].values, df['x2'].values, y_pred):
-        plt.annotate(str(round(y_, 2)),
-                     (x_1, x_2), 
-                     textcoords='offset points',
-                     xytext=(0,5),
-                     ha='center') 
-    # Saving the image to a file, and showing it as well
-    plt.savefig('k' + str(k) + '.png')
-    plt.show()
-
-for alpha in (0.1, 3, 100):
-    # TODO: Print the loss for each chart.
-    plot_kernel_preds(alpha)
-
-for k in (1, 5, len(X)-1):
-    # TODO: Print the loss for each chart.
-    plot_knn_preds(k)