from sklearn.datasets import load_digits
from sklearn.neural_network import MLPClassifier, MLPRegressor
from sklearn.model_selection import train_test_split

import numpy as np
import matplotlib.pyplot as plt


np.random.seed(2022)

## PART A: CLASSIFICATION
digits = load_digits(n_class=10)
X, y = digits.data, digits.target

n_samples, n_features = X.shape

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.4
)

# Fit MLPClassifier to the training data

# Use the trained MLPClassifier to predict the class labels

# Compute the accuracy on the test set



## PART B: REGRESSION
n = 300
xtrain = np.sort(np.random.rand(n))
ytrain = np.sin(9*xtrain) + xtrain + np.sqrt(1/5.0)*np.random.randn(n)
xtest = np.linspace(0,1,1001)
ytest = np.sin(9*xtest) + xtest

# Fit MLPRegressor to training data.
# You will need to use xtrain.reshape(-1,1) when fitting.

# Use the trained MLPRegressor to predict regression outputs
# You will need to use xtest.reshape(-1,1) when predicting

# Compute mean squared error on test set, plot network output.