def main():
if db in [
'lorenz', 'chua', 'duffing', 'nose_hoover', 'rikitake', 'rossler',
'wang'
]:
x, y, z = tsg.chaoticSystem(samples=samples, systemType=db)
ux = np.array([x[i - L:i] for i in range(L, len(x))])
uy = np.array([y[i - L:i] for i in range(L, len(y))])
u = np.concatenate((ux, uy), axis=1) # INPUT
d = np.array([z[i] for i in range(L, len(z))]).reshape(-1, 1)
dmin = d.min() - (d.max() - d.min()) * 0.1
dmax = d.max() + (d.max() - d.min()) * 0.1
print('Input', u.shape, 'Output', d.shape)
else:
print('Unknown dataset')
return
if kaf == 'KRLS_ALD':
from KAF import KRLS_ALD
f = KRLS_ALD(sigma=sigma, epsilon=eps)
elif kaf == 'QKLMS_AKB':
from KAF import QKLMS_AKB
f = QKLMS_AKB(sigma_init=sigma, epsilon=eps, K=5)
elif kaf == 'ALDKRLS_AKB_2':
from KAF import ALDKRLS_AKB_2
f = ALDKRLS_AKB_2(sigma=sigma, epsilon=eps, K_akb=10)
elif kaf == 'QKLMS_AMK':
from KAF import QKLMS_AMK
f = QKLMS_AMK(sigma=sigma, epsilon=eps)
else:
print('Unknown KAF')
return
fig = plt.figure()
ax = fig.add_subplot(111)
y = np.zeros(d.shape)
CB_ant = 0
for n in range(len(u)):
y[n] = f.evaluate(u[n].reshape(1, -1), d[n].reshape(1, -1))[0]
if n > 3:
ax.plot(np.array([n - 1, n]),
d[n - 2:n],
color='blue',
label='Target')
ax.plot(np.array([n - 1, n]),
y[n - 2:n],
color='red',
label='Predicted')
plt.ylim([dmin, dmax])
plt.pause(1e-3)
if CB_ant != len(f.CB):
ax.scatter(n, y[n], color='black', label='CB update', marker='+')
CB_ant = len(f.CB)
plt.show()
return
def db(samples=5040,system='lorenz',L=40):
import numpy as np
import TimeSeriesGenerator as tsg
x, y, z = tsg.chaoticSystem(samples=samples,systemType=system)
ux = np.array([x[i-L:i] for i in range(L,len(x))])
uy = np.array([y[i-L:i] for i in range(L,len(y))])
u = np.concatenate((ux,uy), axis=1) # INPUT
d = np.array([z[i] for i in range(L,len(z))]).reshape(-1,1)
return u,d
def _get_ts_df():
tsg = TSG.TimeSeriesGenerator()
list_ts = tsg.generate_month_data()
ts = TS.TimeSeries()
ts.set_ts_list(list_ts, ["TIME", "VALUE"])
df = ts.get_ts_df()
return df
def _get_ts_array(time_type):
tsg = TSG.TimeSeriesGenerator()
ts = TS.TimeSeries()
if time_type == "d":
ts.set_ts_list(tsg.generate_day_data(), ["TIME", "VALUE"])
elif time_type == "w":
ts.set_ts_list(tsg.generate_week_data(), ["TIME", "VALUE"])
elif time_type == "m" or time_type == "y":
ts.set_ts_list(tsg.generate_month_data(), ["TIME", "VALUE"])
ts_d = ts.get_ts_df()
return ts_d["TIME"].array
def _get_ts_df(time_type):
tsg = TSG.TimeSeriesGenerator()
ts = TS.TimeSeries()
if time_type == "d":
ts.set_ts_list(tsg.generate_day_data(), ["TIME", "VALUE"])
elif time_type == "w":
ts.set_ts_list(tsg.generate_week_data(), ["TIME", "VALUE"])
elif time_type == "m" or time_type == "y":
ts.set_ts_list(tsg.generate_month_data(), ["TIME", "VALUE"])
ts_df = ts.get_ts_df()
ts_df["VALUE"] = [1] * ts_df.shape[0]
return ts_df
def kafSearch_MC(filterName,systemName,n_samples,trainSplit,MC_runs):
import KAF
from tqdm import tqdm
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
import pandas as pd
import numpy as np
n_paramters = 0
# 1. Generate data for grid search
n_train = int(n_samples*trainSplit)
n_test = n_samples - n_train
folder = 'GridSearchWang2/'
attractors = ['lorenz','wang', 'rossler', 'rikitake']
if systemName in attractors:
signalEmbedding = 5
import TimeSeriesGenerator
inputSignal, y, z = TimeSeriesGenerator.chaoticSystem(samples=(n_samples+signalEmbedding)*MC_runs,systemType=systemName)
inputSignal -= inputSignal.mean()
inputSignal /= inputSignal.std()
y -= y.mean()
y /= y.std()
z -= z.mean()
z /= z.std()
elif systemName == "4.1":
signalEmbedding = 5
import testSystems as ts
inputSignal, targetSignal = ts.testSystems(samples = (n_samples+signalEmbedding)*MC_runs, systemType = "4.1_AKB")
# inputSignal -= inputSignal.mean()
# inputSignal /= inputSignal.std()
elif systemName == "4.2":
signalEmbedding = 2
import testSystems as ts
inputSignal = ts.testSystems(samples = (n_samples+signalEmbedding)*MC_runs, systemType = "4.2_AKB")
inputSignal -= inputSignal.mean()
inputSignal /= inputSignal.std()
else:
raise ValueError("Database does not exist")
# 2. Pick filter
if filterName == "QKLMS":
# 2.1. Generate parameters for QKLMS grid search
if systemName in attractors:
eta = [0.05, 0.1, 0.3, 0.5, 0.9]
sigma = [1e-2, 1e-1, 1, 2, 4, 8]
epsilon = [1e-1, 0.5, 1, 2]
elif systemName == "chua":
eta = [0.05, 0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 0, 2, 4]
epsilon = [1e-2, 1e-1, 1,2]
elif systemName == "4.1":
eta = [0.05, 0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 0, 2, 4]
epsilon = [1e-2, 1e-1, 1,2]
elif systemName == "4.2":
eta = [0.05, 0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 0, 2, 4]
epsilon = [1e-2, 1e-1, 1,2]
params = [{'eta':et,'epsilon':ep, 'sigma':s } for et in eta for ep in epsilon for s in sigma]
# 2.2. Search over QKLMS with Montecarlo Simulation
results = []
testing_mse_xRun = []
CB_size_xRun = []
tradeOff_dist_xRun = []
signalPower = inputSignal.var()
singleRunDataSize = n_samples
trainLength = n_train
for run in range(MC_runs):
print("\n\nRunning Monte Carlo simulation #{}...\n".format(run))
try:
u_train, u_test, d_train, d_test = trainAndTestSplitWithEmbedding(inputSignal, targetSignal,signalEmbedding, run, singleRunDataSize, trainLength)
except:
u_train, u_test, d_train, d_test = customEmbeddingForKAFs(inputSignal, signalEmbedding, run, singleRunDataSize, trainLength)
# u_train, u_test, d_train, d_test = customExogenousEmbeddingForKAFs(inputSignal, y, z, signalEmbedding, run, singleRunDataSize, trainLength)
for p in tqdm(params):
try:
f = KAF.QKLMS(eta=p['eta'],epsilon=p['epsilon'],sigma=p['sigma'])
y = f.evaluate(u_train,d_train)
y_pred = f.predict(u_test)
err = d_test-y_pred.reshape(-1,1)
t_mse = np.mean(err**2)
testing_mse_xRun.append(t_mse/signalPower)
CB_size_xRun.append(len(f.CB))
except:
testing_mse_xRun.append(np.nan)
CB_size_xRun.append(np.nan)
allTestingMSEs = np.array(testing_mse_xRun).reshape(MC_runs,-1)
allCB_sizes = np.array(CB_size_xRun).reshape(MC_runs,-1)
testing_mse_average = np.nanmean(allTestingMSEs, axis=0)
CB_size_average = np.nanmean(allCB_sizes, axis=0)
tradeOff_distance = [tradeOffDistance(testing_mse_average[j],CB_size_average[j]/n_train) for j in range(len(params))]
results = [p for p in params]
results_df = pd.DataFrame(data=results)
results_df['testing_mse'] = testing_mse_average
results_df['CB_size'] = CB_size_average
results_df['CB_size'] = results_df['CB_size'].astype(int)
results_df['tradeOff_dist'] = tradeOff_distance
results_df.to_csv(folder + filterName + '_' + systemName + '_' + str(n_samples) + '.csv')
elif filterName == "QKLMS_AKB":
# 2.1. Generate parameters for QKLMS_AKB grid search
import numpy as np
if systemName in attractors:
eta = [0.05, 0.1, 0.3, 0.9]
sigma = [ 1e-1, 1, 2]
epsilon = [1e-1, 0.5, 1, 2]
mu = [1e-3, 1e-1, 0.1, 1]
K = [2,5,10,20]
elif systemName == "chua":
eta = [0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 2, 4]
epsilon = [1e-2, 1e-1, 1]
mu = [1e-3, 1e-1, 0.1, 1]
K = [2,5,10,20]
elif systemName == "4.1":
eta = [0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 2, 4]
epsilon = [1e-2, 1e-1, 1]
mu = [1e-3, 1e-1, 0.1, 1]
K = [2,5,10,20]
elif systemName == "4.2":
eta = [0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 2, 4]
epsilon = [1e-2, 1e-1, 1]
mu = [1e-3, 1e-1, 0.1, 1]
K = [2,5,10,20]
params = [{'eta':et,'epsilon':ep, 'sigma_init':s, 'mu':m, 'K':int(k) } for et in eta for ep in epsilon for s in sigma for m in mu for k in K]
# 2.2. Search over QKLMS AKB with Montecarlo Simulation
results = []
testing_mse_xRun = []
CB_size_xRun = []
tradeOff_dist_xRun = []
signalPower = inputSignal.var()
singleRunDataSize = n_samples
trainLength = n_train
for run in range(MC_runs):
print("\n\nRunning Monte Carlo simulation #{}...\n".format(run))
try:
u_train, u_test, d_train, d_test = trainAndTestSplitWithEmbedding(inputSignal, targetSignal,signalEmbedding, run, singleRunDataSize, trainLength)
except:
# u_train, u_test, d_train, d_test = customEmbeddingForKAFs(inputSignal, signalEmbedding, run, singleRunDataSize, trainLength)
u_train, u_test, d_train, d_test = customExogenousEmbeddingForKAFs(inputSignal, y, z, signalEmbedding, run, singleRunDataSize, trainLength)
for p in tqdm(params):
try:
f = KAF.QKLMS_AKB(eta=p['eta'],epsilon=p['epsilon'],sigma_init=p['sigma_init'], mu=p['mu'], K=p['K'])
y = f.evaluate(u_train,d_train)
y_pred = f.predict(u_test)
err = d_test-y_pred.reshape(-1,1)
t_mse = np.mean(err**2)
testing_mse_xRun.append(t_mse/signalPower)
CB_size_xRun.append(len(f.CB))
except:
testing_mse_xRun.append(np.nan)
CB_size_xRun.append(np.nan)
allTestingMSEs = np.array(testing_mse_xRun).reshape(MC_runs,-1)
allCB_sizes = np.array(CB_size_xRun).reshape(MC_runs,-1)
testing_mse_average = np.nanmean(allTestingMSEs, axis=0)
CB_size_average = np.nanmean(allCB_sizes, axis=0)
tradeOff_distance = [tradeOffDistance(testing_mse_average[j],CB_size_average[j]/n_train) for j in range(len(params))]
results = [p for p in params]
results_df = pd.DataFrame(data=results)
results_df['testing_mse'] = testing_mse_average
results_df['CB_size'] = CB_size_average
results_df['CB_size'] = results_df['CB_size'].astype(int)
results_df['tradeOff_dist'] = tradeOff_distance
results_df.to_csv(folder + filterName + '_' + systemName + '_' + str(n_samples) + '.csv')
elif filterName == "QKLMS_AMK":
# 2.1. Generate parameters for QKLMS_AKB grid search
import numpy as np
if systemName in attractors:
mu = [0.1, 0.5,1,1.5]
eta = [0.05, 0.1, 0.3, 0.5, 0.9]
epsilon = [1e-1, 0.5, 1, 2]
K = [5,10,15,20]
elif systemName == "chua":
eta = np.linspace(0.02,1,n_paramters)
epsilon = np.linspace(1e-3,100,n_paramters)
mu = np.linspace(1e-4,1,n_paramters)
K = np.linspace(2,20,n_paramters)
elif systemName == "4.1":
mu = [1e-3,1e-1, 0.1,1]
eta = [0.05, 0.1,0.5,0.9]
epsilon = [1e-2, 1e-1, 1, 1.5, 2]
K = [2,5,10,15,20]
elif systemName == "4.2":
mu = [1e-3,1e-1, 0.1,1]
eta = [0.05, 0.1,0.5,0.9]
epsilon = [1e-2, 1e-1, 1, 1.5, 2]
K = [2,5,10,15,20]
params = [{'eta':et,'epsilon':ep, 'mu':m, 'K':int(k)} for et in eta for ep in epsilon for m in mu for k in K]
# 2.2. Search over QKLMS_AMK with Montecarlo Simulation
results = []
testing_mse_xRun = []
CB_size_xRun = []
tradeOff_dist_xRun = []
signalPower = inputSignal.var()
singleRunDataSize = n_samples
trainLength = n_train
for run in range(MC_runs):
print("\n\n Running Monte Carlo simulation #{}...\n".format(run))
try:
u_train, u_test, d_train, d_test = trainAndTestSplitWithEmbedding(inputSignal, targetSignal,signalEmbedding, run, singleRunDataSize, trainLength)
except:
u_train, u_test, d_train, d_test = customEmbeddingForKAFs(inputSignal, signalEmbedding, run, singleRunDataSize, trainLength)
# u_train, u_test, d_train, d_test = customExogenousEmbeddingForKAFs(inputSignal, y, z, signalEmbedding, run, singleRunDataSize, trainLength)
for p in tqdm(params):
try:
f = KAF.QKLMS_AMK(eta=p['eta'],epsilon=p['epsilon'], mu=p['mu'], Ka=p['K'], A_init="pca")
y = f.evaluate(u_train,d_train)
y_pred = f.predict(u_test)
err = d_test-y_pred.reshape(-1,1)
t_mse = np.mean(err**2)
testing_mse_xRun.append(t_mse/signalPower)
CB_size_xRun.append(len(f.CB))
except:
testing_mse_xRun.append(np.nan)
CB_size_xRun.append(np.nan)
allTestingMSEs = np.array(testing_mse_xRun).reshape(MC_runs,-1)
allCB_sizes = np.array(CB_size_xRun).reshape(MC_runs,-1)
testing_mse_average = np.nanmean(allTestingMSEs, axis=0)
CB_size_average = np.nanmean(allCB_sizes, axis=0)
tradeOff_distance = [tradeOffDistance(testing_mse_average[j],CB_size_average[j]/n_train) for j in range(len(params))]
results = [p for p in params]
results_df = pd.DataFrame(data=results)
results_df['testing_mse'] = testing_mse_average
results_df['CB_size'] = CB_size_average
results_df['CB_size'] = results_df['CB_size'].astype(int)
results_df['tradeOff_dist'] = tradeOff_distance
results_df.to_csv(folder + filterName + '_' + systemName + '_' + str(n_samples) + '.csv')
else:
raise ValueError("Filter does not exist")
return
def kafSearch(filterName,systemName,n_samples,trainSplit):
import KAF
from tqdm import tqdm
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
import pandas as pd
import numpy as np
n_paramters = 0
# 1. Generate data for grid search
n_train = int(n_samples*trainSplit)
n_test = n_samples - n_train
if systemName == "lorenz" or systemName == "chua":
embedding = 5
import TimeSeriesGenerator
x, y, z = TimeSeriesGenerator.chaoticSystem(samples=200000,systemType='lorenz')
x -= x.mean()
x /= x.std()
u_train = np.array([x[i-embedding:i] for i in range(embedding,n_train)])
d_train = np.array([x[i] for i in range(embedding,n_train)]).reshape(-1,1)
u_test = np.array([x[i-embedding:i] for i in range(n_train,n_samples)])
d_test = np.array([x[i] for i in range(n_train,n_samples)]).reshape(-1,1)
elif systemName == "4.2":
embedding = 2
import testSystems as ts
x = ts.testSystems(samples = n_samples+embedding, systemType = "4.2_AKB")
x -= x.mean()
x /= x.std()
u_train = np.array([x[i-embedding:i] for i in range(embedding,n_train)])
d_train = np.array([x[i] for i in range(embedding,n_train)]).reshape(-1,1)
u_test = np.array([x[i-embedding:i] for i in range(n_train,n_samples)])
d_test = np.array([x[i] for i in range(n_train,n_samples)]).reshape(-1,1)
else:
raise ValueError("Database does not exist")
# 2. Pick filter
if filterName == "QKLMS":
# 2.1. Generate parameters for QKLMS grid search
import numpy as np
if systemName == "lorenz":
eta = [0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 0, 2, 4]
epsilon = [1e-2, 1e-1, 1]
elif systemName == "chua":
# eta = np.linspace(0.1,0.9,n_paramters)
# sigma = np.linspace(100,500,n_paramters)
# epsilon = np.linspace(0.01,20,n_paramters)
a = 0
elif systemName == "4.2":
eta = [0.1, 0.5, 0.9]
sigma = [1e-2, 1e-1, 0, 2, 4]
epsilon = [1e-2, 1e-1, 1]
params = [{'eta':et,'epsilon':ep, 'sigma':s } for et in eta for ep in epsilon for s in sigma]
# 2.2. Search over QKLMS
results = []
for p in tqdm(params):
try:
f = KAF.QKLMS(eta=p['eta'],epsilon=p['epsilon'],sigma=p['sigma'])
y = f.evaluate(u_train,d_train)
y_pred = f.predict(u_test)
err = d_test-y_pred.reshape(-1,1)
t_mse = np.mean(err**2)
signalPower = x.var()
p['testing_mse'] = t_mse/signalPower
p['CB_size'] = len(f.CB)
p['tradeOff_dist'] = tradeOffDistance(p['testing_mse'],p['CB_size']/n_train)
except:
p['testing_mse'] = np.nan
p['CB_size'] = np.nan
p['tradeOff_dist'] = np.nan
results.append(p)
pd.DataFrame(data=results).to_csv('GridSearchResults2ndRun/' + filterName + '_' + systemName + '_' + str(n_samples) + '.csv')
elif filterName == "QKLMS_AKB":
# 2.1. Generate parameters for QKLMS_AKB grid search
import numpy as np
if systemName == "lorenz":
eta = [0.1, 0.9]
sigma = [1e-2, 1e-1, 2]
epsilon = [1e-2, 1e-1, 1]
mu = [1e-3, 1e-1, 0.1,1]
K = [2,5,10,20]
elif systemName == "chua":
eta = np.linspace(0.1,0.9,n_paramters)
sigma = np.linspace(0.01,200,n_paramters)
epsilon = np.linspace(1e-3,100,n_paramters)
mu = np.linspace(1e-4,1,n_paramters)
K = np.linspace(2,20,n_paramters)
elif systemName == "4.2":
eta = np.linspace(0.1,0.9,n_paramters)
sigma = np.linspace(0.01,200,n_paramters)
epsilon = np.linspace(1e-3,100,n_paramters)
mu = np.linspace(1e-4,1,n_paramters)
K = np.linspace(2,20,n_paramters)
params = [{'eta':et,'epsilon':ep, 'sigma_init':s, 'mu':m, 'K':int(k) } for et in eta for ep in epsilon for s in sigma for m in mu for k in K]
# 2.2. Search over QKLMS
results = []
for p in tqdm(params):
try:
f = KAF.QKLMS_AKB(eta=p['eta'],epsilon=p['epsilon'],sigma_init=p['sigma_init'], mu=p['mu'], K=p['K'])
y = f.evaluate(u_train,d_train)
y_pred = f.predict(u_test)
err = d_test-y_pred.reshape(-1,1)
t_mse = np.mean(err**2)
signalPower = x.var()
p['testing_mse'] = t_mse/signalPower
p['CB_size'] = len(f.CB)
p['tradeOff_dist'] = tradeOffDistance(p['testing_mse'],p['CB_size']/n_train)
except:
p['testing_mse'] = np.nan
p['CB_size'] = np.nan
p['tradeOff_dist'] = np.nan
results.append(p)
pd.DataFrame(data=results).to_csv('GridSearchResults2ndRun/' + filterName + '_' + systemName + '_' + str(n_samples) + '.csv')
elif filterName == "QKLMS_AMK":
# 2.1. Generate parameters for QKLMS_AKB grid search
import numpy as np
if systemName == "lorenz":
mu = [1e-3,1e-1, 0.1,1,1.5,2]
eta = [0.05, 0.1,0.5,0.9]
epsilon = [1e-2, 1e-1, 1, 1.5, 2]
K = [2,5,10,15,20]
elif systemName == "chua":
eta = np.linspace(0.02,1,n_paramters)
epsilon = np.linspace(1e-3,100,n_paramters)
mu = np.linspace(1e-4,1,n_paramters)
K = np.linspace(2,20,n_paramters)
elif systemName == "4.2":
mu = [1e-3,1e-1, 0.1,1]
eta = [0.05, 0.1,0.5,0.9]
epsilon = [1e-2, 1e-1, 1, 1.5, 2]
K = [2,5,10,15,20]
params = [{'eta':et,'epsilon':ep, 'mu':m, 'K':int(k)} for et in eta for ep in epsilon for m in mu for k in K]
# 2.2. Search over QKLMS_AMK
results = []
for p in tqdm(params):
try:
f = KAF.QKLMS_AMK(eta=p['eta'],epsilon=p['epsilon'], mu=p['mu'], Ka=p['K'], A_init="pca")
y = f.evaluate(u_train,d_train)
y_pred = f.predict(u_test)
err = d_test-y_pred.reshape(-1,1)
t_mse = np.mean(err**2)
signalPower = x.var()
p['testing_mse'] = t_mse/signalPower
p['CB_size'] = len(f.CB)
p['tradeOff_dist'] = tradeOffDistance(p['testing_mse'],p['CB_size']/n_train)
except:
p['testing_mse'] = np.nan
p['CB_size'] = np.nan
p['tradeOff_dist'] = np.nan
results.append(p)
pd.DataFrame(data=results).to_csv('GridSearchResults2ndRun/' + filterName + '_' + systemName + '_' + str(n_samples) + '.csv')
else:
raise ValueError("Filter does not exist")
return
Created on Mon Dec 21 23:17:34 2020 @author: USUARIO """ import TimeSeriesGenerator as tsg import numpy as np import search import pandas as pd samples = 500 batchSize = 10 # epList = np.logspace(0, 2, 20) # sgmList = np.logspace(0,2,20) """ATRACTOR DE LORENZ""" x, y, z = tsg.chaoticSystem(samples=samples + 10, systemType="lorenz") ua = x[-samples - 2:-2].reshape(-1, 1) ub = y[-samples - 3:-3].reshape(-1, 1) u = np.concatenate((ua, ub), axis=1) # INPUT d = z[-samples - 1:-1].reshape(-1, 1) #TARGET epList = np.linspace(1e-1, 300, 300) sR_QKLMS = search.searchQKLMS(u, d, epList, "lorenz") sgmList = np.linspace(0.1, np.max(u), 20) epList = np.linspace(1e-6, 1e-1, 20) sR_KRLS = search.searchKRLS_ALD(u, d, sgmList, epList, "lorenz") sgmList = np.linspace(0.1, np.max(u), 20) epList = np.linspace(1e-1, 300, 300) sR_QKLMS_base = search.searchQKLMS_base(u, d, sgmList, epList, "lorenz")
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 21 00:28:14 2021
@author: USUARIO
"""
import numpy as np
import testSystems as ts
N = 5000
u,d = ts.testSystems(N,'4.1_AKB')
import TimeSeriesGenerator
x, y, z = TimeSeriesGenerator.chaoticSystem(samples=6000,systemType='wang')
def std(x):
x = x - x.mean()
return x/x.std()
# signalEmbedding = 5
# X = np.array([u[i-signalEmbedding:i] for i in range(signalEmbedding,len(u))])
# y = np.array([d[i] for i in range(signalEmbedding,len(u))]).reshape(-1,1)
u = d = std(x)
import KAF
f = KAF.QKLMS_AMK(eta=0.01,epsilon=0.5,embedding=10, Ka=10, mu=0.05)
f.evaluate(u[:100],d[:100])
f.fit(u[:4000],d[:4000])
from sklearn.mixture import BayesianGaussianMixture as bgm
from sklearn.linear_model import LinearRegression
from scipy.spatial.distance import cdist
from sklearn.metrics import r2_score
import TimeSeriesGenerator as tsg
import numpy as np
samples = 10000
batch_size = 50 #Batch size
attr_list = [
"chua", "duffing", "nose_hoover", "rikitake", "rossler", "wang", "lorenz"
]
for att_type in attr_list:
x, y, z = tsg.chaoticSystem(samples=samples + 10, systemType=att_type)
u = np.concatenate(
(x[-samples - 2:-2].reshape(-1, 1), y[-samples - 3:-3].reshape(-1, 1)),
axis=1) # INPUT
d = z[-samples - 1:-1].reshape(-1, 1) #TARGET
# Training data dimensions
Nu, Du = u.shape
Nd, Dd = d.shape
#inicializacion
scr = [] #Score
"""PRETRAIN STAGE"""
#split data in batches
u_train = u.reshape(-1, batch_size, Du)
d_train = d.reshape(-1, batch_size, Dd)
db = 'lorenz'
R = 10 #MC repetions
tsLength = 1000
trainLength = int(tsLength * 0.7)
testLength = tsLength - trainLength
samples = R * tsLength
sigma = 1
eps = 1e-4
scale = 5 #For plotting purposes
x, y, z = tsg.chaoticSystem(samples=samples + L, systemType=db)
x -= x.mean()
x /= x.std()
mse = []
for r in range(R):
#print(L+r*tsLength,L+r*tsLength+trainLength,L+r*tsLength+trainLength,L+(r+1)*tsLength)
u_train = np.array([
x[i - L:i]
for i in range(L + r * tsLength, L + r * tsLength + trainLength)
])
d_train = np.array([
x[i] for i in range(L + r * tsLength, L + r * tsLength + trainLength)
]).reshape(-1, 1)