vnegi10

"""
    run_training(loss_change,
                 learn, 
	         x_in, 
	         y_in)

Run training epochs until the Δloss ≤ loss_change.
"""
function run_training(loss_change,
                      learn, 

vnegi10

function update_model!(learn, 
	               flux_model,
	               x_in, 
	               y_in)

    dLdm, _, _ = gradient(get_loss, flux_model, x_in, y_in)
    @. flux_model.weight = flux_model.weight - Float32(learn * dLdm.weight)
    @. flux_model.bias   = flux_model.bias - Float32(learn * dLdm.bias)

    return flux_model

vnegi10

function get_loss(flux_model, x_in, y_in)

    y_model = flux_model(x_in)
    mse_error = Flux.mse(y_model, y_in)

    return mse_error
end

vnegi10

function plot_daily_gas_usage(df_all_gas::DataFrame,
                              month::String,
                              year::Int64)

    df_gas = process_gas_df(df_all_gas)

    df_gas = filter(row -> occursin(month, Dates.monthname(row.Date)) &&
	                            Dates.year(row.Date) == year,
		                    df_gas)

vnegi10

function process_gas_df(df_all_gas::DataFrame)

    df_gas = deepcopy(df_all_gas)

    # Temperature is 10x in original data
    df_gas[!, :T] = df_gas[!, :T] ./ 10

    # Convert from kWh to m^3
    conversion_factor = 0.10058
    df_gas[!, :ch] = df_gas[!, :ch] .* conversion_factor

vnegi10

function get_hist_gas_usage()
	
    gas_pointer_dict = get_request(GAS_POINTER)
    page_end = (gas_pointer_dict["value"] / 32) |> ceil |> Int

    all_gas = DataFrame[]

    for page = 1:page_end
	gas_usage_dict = get_request(gas_usage_ep(page))
	df_gas = vcat(DataFrame.(gas_usage_dict["value"])...)

vnegi10

function get_request(endpoint_name::String)

    url = URL
    headers = ["Content-Type" => "application/json"]

    response = HTTP.request(
	                    "GET",
	                    join([URL, endpoint_name]),
		            headers,
	                    verbose = 0,

vnegi10

function delete_rows(db_name::String, table_name::String, price::Float64)

    conn_db = connect_db(db_name)

    try
	DBInterface.execute(conn_db, 
	                   "DELETE FROM $table_name WHERE Price > $price")
    catch
	error("Unable to delete from $table_name")
    finally

vnegi10

function remove_duplicate_fruits(db_name::String, table_name::String)

    conn_db = connect_db(db_name)

    # Remove duplicate rows using a common table expression (cte)
    try
	DBInterface.execute(conn_db, 
		            "WITH cte AS (
	                    SELECT 
                                Name, 

vnegi10

function add_to_fruit_table(conn_master, 
	                    db_name::String, 
	                    table_name::String,
                            df_input::DataFrame)

    conn = create_fruit_table(conn_master, db_name, table_name)

    stmt = try
	       DBInterface.prepare(conn, 
		                   "INSERT INTO $(table_name) VALUES(?, ?, ?)")