From dbaea6f1771af41b4bb642119174eb1526d558a1 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Wed, 15 Jun 2011 00:58:23 +0200
Subject: [PATCH] typo
---
doc/datasets/index.rst | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/doc/datasets/index.rst b/doc/datasets/index.rst
index b5ccc3d2bc..780625ca62 100644
--- a/doc/datasets/index.rst
+++ b/doc/datasets/index.rst
@@ -59,13 +59,13 @@ Scipy sparse CSR matrices are used for ``X`` and numpy arrays are used for ``y``
You may load a dataset like this::
- >>> from scikits.learn.datasets import load_svmlight_format
- >>> X_train, y_train = load_svmlight_format("/path/to/train_dataset.txt")
+ >>> from scikits.learn.datasets import load_svmlight_file
+ >>> X_train, y_train = load_svmlight_file("/path/to/train_dataset.txt")
... # doctest: +SKIP
You may also load two datasets at once::
- >>> X_train, y_train, X_test, y_test = load_svmlight_format(
+ >>> X_train, y_train, X_test, y_test = load_svmlight_file(
... "/path/to/train_dataset.txt",
... "/path/to/test_dataset.txt") # doctest: +SKIP
@@ -73,7 +73,7 @@ In this case, ``X_train`` and ``X_test`` are guaranteed to have the same number
of features. Another way to achieve the same result is to fix the number of
features::
- >>> X_test, y_test = load_svmlight_format(
+ >>> X_test, y_test = load_svmlight_file(
... "/path/to/test_dataset.txt", n_features=X_train.shape[1])
... # doctest: +SKIP
--
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