preetesh33

#!/usr/bin/perl

srand (time());
my $randletter = "(int (rand (26)) + (int (rand (1) + .5) % 2 ? 65 : 97))";
my $salt = sprintf ("%c%c", eval $randletter, eval $randletter);
my $plaintext = shift;
my $crypttext = crypt ($plaintext, $salt);

print "${crypttext}\n";

preetesh33

2020-06-24T16:41:57 [I|app|896c85e1] Started POST "/hosts" for 10.x.x.x at 2020-06-24 16:41:57 +0000
2020-06-24T16:41:57 [I|app|896c85e1] Processing by HostsController#create as */*
2020-06-24T16:41:57 [I|app|896c85e1]   Parameters: {"utf8"=>"✓", "authenticity_token"=>"NtDzbGZDLHLqxcsRiGKFQqrK3pE8Hl+hv84ihPnp0+9qpdGyKlAL9V69XW3MY7h/LmFuMOtbWV8zNgBPzMyvxA==", "host"=>{"name"=>"adam-ranildi", "organization_id"=>"8", "location_id"=>"2", "hostgroup_id"=>"1", "content_facet_attributes"=>{"lifecycle_environment_id"=>"4", "content_view_id"=>"4", "content_source_id"=>"1"}, "puppetclass_ids"=>[""], "managed"=>"true", "progress_report_id"=>"[FILTERED]", "type"=>"Host::Managed", "interfaces_attributes"=>{"0"=>{"_destroy"=>"0", "type"=>"Nic::Managed", "mac"=>"", "identifier"=>"", "name"=>"adam-ranildi", "domain_id"=>"2", "subnet_id"=>"1", "ip"=>"10.x.x.x", "ip6"=>"", "managed"=>"1", "primary"=>"1", "provision"=>"1", "virtual"=>"0", "tag"=>"", "attached_to"=>"", "compute_attributes"=>{"type"=>"VirtualE1000", "network"=>"n

preetesh33

ipa-replica-install --setup-ca --setup-dns --mkhomedir --no-ntp --auto-reverse
ipa-replica-install --setup-ca --setup-dns --mkhomedir --no-ntp --auto-reverse --no-forwarders

preetesh33

import requests
from bs4 import BeautifulSoup
import smtplib
import time
import sys

# enable secure login in gmail using url https://www.google.com/settings/security/lesssecureapps

#### add expected price
expected_price = 5000

preetesh33

import pandas as pd
from mlforecast import MLForecast
from xgboost import XGBRegressor
from window_ops.rolling import rolling_mean, rolling_max, rolling_min, rolling_std
from mlforecast.forecast import MLForecast
from mlforecast import MLForecast
import os
from joblib import dump
import json

preetesh33

import pandas as pd
import pandas as pd
from joblib import load
import json
from datetime import datetime
import warnings
import logging
import yaml

preetesh33

import pandas as pd
import pandas as pd
from joblib import load
import json
from datetime import datetime
import warnings


warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning)

preetesh33

To add seasonality to your forecasting model, you can use Fourier terms. Fourier terms can capture seasonality of any period and are especially useful when the period of the seasonality is large. 

In the `MLForecast` class, you can add Fourier terms using the `seasonality` parameter in the `fit` method. The `seasonality` parameter is a dictionary where the keys are the periods of the seasonality and the values are the order of the Fourier series.

Here's how you can modify your code to add weekly and yearly seasonality:

```python
# Assuming your data is in minutes and you want to capture weekly and yearly seasonality
weekly_seasonality = 60 * 24 * 7  # minutes in a week
yearly_seasonality = 60 * 24 * 365  # minutes in a non-leap year