genomexyz

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

ranx = np.arange(0, 1500)
rany =  np.random.rand(1500)


ranx_label = [''] * len(ranx)
ranx_label[0] = 'labelx awal'

genomexyz

import torch
import argparse
import torch.optim as optim
import torch.nn.functional as F
import sys
import random
import pandas as pd
import numpy as np
sys.path.append(".")
from utils import *

genomexyz

import torch
import argparse
import torch.optim as optim
import torch.nn.functional as F
import sys
import random
import pandas as pd
import numpy as np
sys.path.append(".")
from utils import *

genomexyz

import numpy as np
from sklearn.linear_model import LinearRegression, Ridge, Lasso, LassoLars, BayesianRidge
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor
from sklearn.metrics import mean_absolute_error

#setting
param_filename = 'all_param.npy'
label_filename = 'all_label.npy'
testing_data_num = 600

genomexyz

import numpy as np

#setting
seq_len = 5
index_missing = 2 #python index start from 0, remember

data_jan = np.load('timeseries_metar_windspeed_WADL-2020-1.npy')
data_feb = np.load('timeseries_metar_windspeed_WADL-2020-2.npy')
data_mar = np.load('timeseries_metar_windspeed_WADL-2020-3.npy')

genomexyz

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

data_jan = np.load('timeseries_metar_windspeed_WADL-2020-1.npy')
data_feb = np.load('timeseries_metar_windspeed_WADL-2020-2.npy')
data_mar = np.load('timeseries_metar_windspeed_WADL-2020-3.npy')

alldata = np.hstack((data_jan, data_feb))
alldata = np.hstack((alldata, data_mar))

genomexyz

import pycurl
import sys
import numpy as np
from datetime import datetime, timedelta

#setting
wilayah = 'WADL'
tahun = '2019'
bulan = '10'
timestep = 30

genomexyz

testing_data_num = 96

all_param = np.load(param_filename)[:3400]
all_label = np.load(label_filename)[:3400]

genomexyz

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

#setting
testing_data_num = 10000
epochs = 1000
batch_size = 100

genomexyz

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.metrics import mean_squared_error

#setting
testing_data_num = 10000
epochs = 1000