alexhallam

theme_set(theme_538)
palette = ["#000000", "#ee1d52"]
df_SEAS = SEAS
df_SEAS = df_SEAS.assign(ds = lambda df: pd.to_datetime(df.ds))
df_actuals_forecasts_n = pd.concat([df_SEAS, df_sn])
p = (
    ggplot(df_actuals_forecasts_n, aes(x="ds", y="y"))
    + geom_line(aes(y = 'y'), color = palette[0])
    + geom_point(aes(y = 'y_sim'), color = palette[1], size = 0.1, alpha = 0.1)
    + scale_x_datetime(breaks=date_breaks("1 week"))

alexhallam

import tablespoon as tbsp
from tablespoon.data import SEAS

sn = tbsp.Snaive()
df_sn = sn.predict(
    SEAS, horizon=7 * 4, frequency="D", lag=7, uncertainty_samples=800
).assign(model="snaive")

df_sn.head(10)

alexhallam

theme_set(theme_538)
palette = ["#000000", "#ee1d52"]
df_actuals_forecasts_n = pd.concat([df_APPLE, df_n])
p = (
    ggplot(df_actuals_forecasts_n, aes(x="ds", y="y"))
    + geom_line(aes(y = 'y'), color = palette[0])
    + geom_point(aes(y = 'y_sim'), color = palette[1], size = 0.1, alpha = 0.1)
    + scale_x_datetime(breaks=date_breaks("1 month"))
    + theme(axis_text_x=element_text(angle=45))
    + xlab("")

alexhallam

n = tbsp.Naive()
df_n = (n.predict(df_APPLE, horizon=7*4, frequency="D", lag = 1, uncertainty_samples = 500).assign(model = 'naive'))
df_n.head()

alexhallam

import pandas as pd
import tablespoon as tbsp
from tablespoon.data import APPL
from mizani.breaks import date_breaks
from plotnine import *
from datetime import datetime


# make date string a date object
df_APPLE = APPL

alexhallam

import torch
import torch.nn as nn
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from torch.distributions.normal import Normal
from torch.distributions.uniform import Uniform
from pprint import pprint
# 0) Prepare data
X_numpy, y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)

alexhallam

import numpy as np


font = {
    "A": np.array(
        [
            [0, 0, 1, 0, 0],
            [0, 1, 0, 1, 0],
            [1, 0, 0, 0, 1],
            [1, 0, 0, 0, 1],

alexhallam

import numpy as np
from itertools import product
from multiprocessing import Pool


def smash(a_tuple):
    """
    smash some strings and numbers together and return final string
    """
    return a_tuple[0] + str(a_tuple[1]) + a_tuple[2] + str(a_tuple[3])

alexhallam

use directories::{BaseDirs};
use std::fs::{create_dir};

fn main() {
    if let Some(base_dirs) = BaseDirs::new() {
        let config_path = base_dirs.config_dir();
        let more_path = create_dir(config_path + "/some/dir").unwrap();
        println!("{:?}",base_dirs.config_dir());
    // Linux: $XDG_CONFIG_HOME or $HOME/.config
    // Windows: {FOLDERID_RoamingAppData}

alexhallam

# calculate the sum of a, b, b2
n <- 1000
m <- 10
a <- rnorm(n, 10)
b <- rnorm(n, 20)
b2 <- .1*a + .1*b
c <- sample(a, size = m*n, replace=T) + sample(b, size = m*n, replace=T) + sample(b2, size = m*n, replace=T)