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fix: typo grammar

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slowy07 2021-08-03 08:37:45 +07:00
parent b75844e07c
commit 05aa1fdd37
5 changed files with 7 additions and 7 deletions
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6
R/R/prophet.R
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@ -300,7 +300,7 @@ time_diff <- function(ds1, ds2, units = "days") {
#' Prepare dataframe for fitting or predicting.
#'
#' Adds a time index and scales y. Creates auxillary columns 't', 't_ix',
#' Adds a time index and scales y. Creates auxiliary columns 't', 't_ix',
#' 'y_scaled', and 'cap_scaled'. These columns are used during both fitting
#' and predicting.
#'
@ -816,7 +816,7 @@ add_country_holidays <- function(m, country_name) {
#'
#' @return List with items
#' seasonal.features: Dataframe with regressor features,
#' prior.scales: Array of prior scales for each colum of the features
#' prior.scales: Array of prior scales for each column of the features
#' dataframe.
#' component.cols: Dataframe with indicators for which regression components
#' correspond to which columns.
@ -1648,7 +1648,7 @@ sample_predictive_trend <- function(model, df, iteration) {
#' @export
make_future_dataframe <- function(m, periods, freq = 'day',
include_history = TRUE) {
# For backwards compatability with previous zoo date type,
# For backwards compatibility with previous zoo date type,
if (freq == 'm') {
freq <- 'month'
}

2
R/man/make_all_seasonality_features.Rd
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@ -16,7 +16,7 @@ added regressors.}
\value{
List with items
seasonal.features: Dataframe with regressor features,
prior.scales: Array of prior scales for each colum of the features
prior.scales: Array of prior scales for each column of the features
dataframe.
component.cols: Dataframe with indicators for which regression components
correspond to which columns.

2
R/man/setup_dataframe.Rd
View file

@ -18,7 +18,7 @@ specified additional regressors must also be present.}
list with items 'df' and 'm'.
}
\description{
Adds a time index and scales y. Creates auxillary columns 't', 't_ix',
Adds a time index and scales y. Creates auxiliary columns 't', 't_ix',
'y_scaled', and 'cap_scaled'. These columns are used during both fitting
and predicting.
}

2
docs/_docs/multiplicative_seasonality.md
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@ -68,7 +68,7 @@ fig = m.plot_components(forecast)
![png](/prophet/static/multiplicative_seasonality_files/multiplicative_seasonality_10_0.png)
With `seasonality_mode='multiplicative'`, holiday effects will also be modeled as multiplicative. Any added seasonalities or extra regressors will by default use whatever `seasonality_mode` is set to, but can be overriden by specifying `mode='additive'` or `mode='multiplicative'` as an argument when adding the seasonality or regressor.
With `seasonality_mode='multiplicative'`, holiday effects will also be modeled as multiplicative. Any added seasonalities or extra regressors will by default use whatever `seasonality_mode` is set to, but can be overridden by specifying `mode='additive'` or `mode='multiplicative'` as an argument when adding the seasonality or regressor.

2
python/prophet/diagnostics.py
View file

@ -170,7 +170,7 @@ def cross_validation(model, horizon, period=None, initial=None, parallel=None, c
try:
from dask.distributed import get_client
except ImportError as e:
raise ImportError("parallel='dask' requies the optional "
raise ImportError("parallel='dask' requires the optional "
"dependency dask.") from e
pool = get_client()
# delay df and model to avoid large objects in task graph.