from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LinearRegression
import random
%matplotlib inline
random.seed(100)
dataloader = Dataloader('Bike-Sharing-Dataset/hour.csv')
train, val, test = dataloader.getData()
fullData = dataloader.getFullData()
category_features = ['season', 'holiday', 'mnth', 'hr', 'weekday', 'workingday', 'weathersit']
number_features = ['temp', 'atemp', 'hum', 'windspeed']
features= category_features + number_features
target = ['cnt']
sns.set(font_scale=1.0)
fig, axes = plt.subplots(nrows=3,ncols=2)
fig.set_size_inches(15, 15)
sns.boxplot(data=train,y="cnt",orient="v",ax=axes[0][0])
sns.boxplot(data=train,y="cnt",x="mnth",orient="v",ax=axes[0][1])
sns.boxplot(data=train,y="cnt",x="weathersit",orient="v",ax=axes[1][0])
sns.boxplot(data=train,y="cnt",x="workingday",orient="v",ax=axes[1][1])
sns.boxplot(data=train,y="cnt",x="hr",orient="v",ax=axes[2][0])
from dataloader import Dataloader from kalman import Kalman from nengo.processes import Piecewise dt = 0.02 # simulation time step t_rc = 0.01 # membrane RC time constant t_ref = 0.001 # refractory period tau = 0.009 # synapse time constant for standard first-order lowpass filter synapse N_A = 1000 # number of neurons in first population rate_A = 200, 400 # range of maximum firing rates for population A pool = 0 dataloader = Dataloader() kalman = Kalman() trainX, trainY, testX, testY = dataloader.getData() ChN = np.where(np.sum(trainX, axis=1) != 0) trainX = np.squeeze(trainX[ChN, :]) testX = np.squeeze(testX[ChN, :]) A_0, B_0 = kalman.calculate(trainX, trainY, pool=pool, dt=dt, tau=tau) # A_1, B_1 = kalman.calculate(trainX, trainY, pool=1, dt=dt, tau=tau) # kalman.Kalman_Filter(testX, testY) # kalman.standard_Kalman_Filter(testX, testY) #