#!/usr/bin/env python
# coding: utf-8

# # Stock Price Prediction using Echo State Networks
# 
# ## Introduction
# 
# The Mackey-Glass system is essentially the differential equation, where we set the parameters to $\alpha = 0.2$, $\beta = 10$, $\gamma = 0.1$ and the time delay $\tau = 17$ in  order to have a mildly chaotic attractor. 
# 
# \begin{align}
# \label{eq:MackeyGlass}
# \dot{y}(t) = \alpha y(t-\tau) / (1 + y(t - \tau)^{\beta}) - \gamma y(t)
# \end{align}

# In[1]:


import numpy as np
from sklearn.metrics import mean_squared_error

from matplotlib import pyplot as plt
plt.rcParams['image.cmap'] = 'jet'
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
get_ipython().run_line_magic('matplotlib', 'inline')

from IPython.display import set_matplotlib_formats
set_matplotlib_formats('png', 'pdf')

from pyrcn.echo_state_network import ESNRegressor


# Load the dataset

# In[2]:


data = np.loadtxt("./dataset/MackeyGlass_t17.txt")


# The first \num{500} samples are visualized.

# In[3]:


_, ax = plt.subplots(figsize=(4.75, 1.65), dpi=1200)
ax.plot(data[:500])
ax.set_xlabel("n")
ax.set_ylabel("X[n]")
plt.grid()


# Standardization -> From here on, we have a numpy array!!!

# In[4]:


data = data / (data.max() - data.min())


# Define Train/Test lengths

# In[5]:


initLen = 100 # number of time steps during which internal activations are washed-out during training
# we consider trainLen including the warming-up period (i.e. internal activations that are washed-out when training)
trainLen = initLen + 1900 # number of time steps during which we train the network
testLen = 2000 # number of time steps during which we test/run the network


# Echo State Network preparation

# In[6]:


esn = ESNRegressor(k_in=1, input_scaling=1.0, spectral_radius=1.2, bias=0.0, ext_bias=False, leakage=1.0,
                   reservoir_size=500, k_res=10, wash_out=0, reservoir_activation='tanh', bi_directional=False,
                   teacher_scaling=1., teacher_shift=0., solver='ridge', beta=1e-4, random_state=0)


# Training and Prediction. Be careful, this can take a longer time!!!
# 
# The lowest MSE obtained with this settings were \num{5.97e-06} for the training set and \num{43.1e-06} for the test set.

# In[7]:


train_in = data[None,0:trainLen]
train_out = data[None,0+1:trainLen+1]
test_in = data[None,trainLen:trainLen+testLen]
test_out = data[None,trainLen+1:trainLen+testLen+1]

train_in, train_out = train_in.T, train_out.T
test_in, test_out = test_in.T, test_out.T

esn.fit(X=train_in, y=train_out)
train_pred = esn.predict(X=train_in)
test_pred = esn.predict(X=test_in)

train_err = mean_squared_error(y_true=train_out, y_pred=train_pred)
test_err = mean_squared_error(y_true=test_out, y_pred=test_pred)

print("Train MSE:\t{0}".format(train_err))
print("Test MSE:\t{0}".format(test_err))


# Prediction of the training set.

# In[8]:


_, ax = plt.subplots(figsize=(4.75, 1.65), dpi=1200)
ax.plot(train_out)
ax.plot(train_pred)
ax.set_xlabel("n")
ax.set_ylabel("X[n]")


# Prediction of the test set.
# 

# In[9]:


_, ax = plt.subplots(figsize=(4.75, 1.65), dpi=1200)
ax.plot(test_out)
ax.plot(test_pred)
ax.set_xlabel("n")
ax.set_ylabel("X[n]")

