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Call_LDA_for_Different_TimeSeries.py
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import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from random import gauss
from LinearDiscriminantAnalysis import LinearDiscriminantAnalysis as LDA_mine
# ============= Time Series 1 ================
time_series = []
for k in range(1, 1001):
time_series.append(1/2*np.sign(np.cos(np.pi/2*k)*np.cos(np.pi/2*(2**(1/2))*k))+1/2)
time_series = np.array(time_series)
plt.plot(time_series[:100])
plt.xlabel('Samples')
plt.ylabel('x_k')
nPast = 10
nFuture = 1
nSamples = len(time_series)-nPast-nFuture
x = []
y = []
x0 = []
x1 = []
for s in range(nSamples):
x.append(time_series[s:s+nPast])
y.append(time_series[s+nPast:s+nPast+nFuture])
if y[s] == 0:
x0.append(x[s])
else:
x1.append(x[s])
x = np.array(x)
y = np.array(y)
x_train, x_test, y_train, y_test = train_test_split(x, y, shuffle=True, test_size=0.3)
model_mine = LDA_mine()
model_mine.fit(x_train, y_train)
y_test_pred_mine = model_mine.predict(x_test)
test_acc = accuracy_score(y_test[:, 0], y_test_pred_mine)
# ========= FROM THE LIBRARY =====
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
model = LDA()
model.fit(x_train, y_train[:, 0])
y_test_pred = model.predict(x_test)
test_acc_lib = accuracy_score(y_test[:, 0], y_test_pred)
# ============= Time Series 2 ================
time_series = []
for k in range(1, 1001):
time_series.append(1/2*np.sign(gauss(0.0, 1.0))+1/2)
time_series = np.array(time_series)
plt.plot(time_series[:100])
plt.xlabel('Samples')
plt.ylabel('x_k')
nPast = 10
nFuture = 1
nSamples = len(time_series)-nPast-nFuture
x = []
y = []
x0 = []
x1 = []
for s in range(nSamples):
x.append(time_series[s:s+nPast])
y.append(time_series[s+nPast:s+nPast+nFuture])
if y[s] == 0:
x0.append(x[s])
else:
x1.append(x[s])
x = np.array(x)
y = np.array(y)
x_train, x_test, y_train, y_test = train_test_split(x, y, shuffle=True, test_size=0.3)
model_mine = LDA_mine()
model_mine.fit(x_train, y_train)
y_test_pred_mine = model_mine.predict(x_test)
test_acc = accuracy_score(y_test[:, 0], y_test_pred_mine)
# ========= FROM THE LIBRARY =====
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
model = LDA()
model.fit(x_train, y_train[:, 0])
y_test_pred = model.predict(x_test)
test_acc_lib = accuracy_score(y_test[:, 0], y_test_pred)
# ============= Logistic Map Function ================
time_series = []
x_k = [0.1]
threshold = 0.02
for k in range(1, 1001):
time_series.append(1/2*np.sign(x_k[-1]-threshold)+1/2)
x_k.append(1*x_k[-1]*(1-x_k[-1]))
time_series = np.array(time_series)
plt.plot(x_k[:100])
plt.plot(time_series[:100])
plt.legend(['x^k', 'x^k_q'])
plt.xlabel('Samples')
nPast = 10
nFuture = 1
nSamples = len(time_series)-nPast-nFuture
x = []
y = []
x0 = []
x1 = []
for s in range(nSamples):
x.append(time_series[s:s+nPast])
y.append(time_series[s+nPast:s+nPast+nFuture])
x = np.array(x)
y = np.array(y)
x_train, x_test, y_train, y_test = train_test_split(x, y, shuffle=True, test_size=0.3)
model_mine = LDA_mine()
model_mine.fit(x_train, y_train)
y_test_pred_mine = model_mine.predict(x_test)
test_acc = accuracy_score(y_test[:, 0], y_test_pred_mine)
# ========= FROM THE LIBRARY =====
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
model = LDA()
model.fit(x_train, y_train[:, 0])
y_test_pred = model.predict(x_test)
test_acc_lib = accuracy_score(y_test[:, 0], y_test_pred)