Commit 8c3e91c8 authored by Wim Taymans's avatar Wim Taymans

gst/gstclock.c: Marked the stats property as unimplemented so people don't get wild ideas.

Original commit message from CVS:
* gst/gstclock.c: (gst_clock_class_init), (do_linear_regression):
Marked the stats property as unimplemented so people don't get
wild ideas.
Add debug message when regression goes wrong.
Added some more docs.
parent c8456d43
2006-03-14 Wim Taymans <wim@fluendo.com>
* gst/gstclock.c: (gst_clock_class_init), (do_linear_regression):
Marked the stats property as unimplemented so people don't get
wild ideas.
Add debug message when regression goes wrong.
Added some more docs.
2006-03-14 Wim Taymans <wim@fluendo.com>
* gst/gstsegment.c: (gst_segment_to_stream_time):
......
......@@ -578,8 +578,9 @@ gst_clock_class_init (GstClockClass * klass)
gobject_class->get_property = GST_DEBUG_FUNCPTR (gst_clock_get_property);
g_object_class_install_property (G_OBJECT_CLASS (klass), PROP_STATS,
g_param_spec_boolean ("stats", "Stats", "Enable clock stats",
DEFAULT_STATS, G_PARAM_READWRITE));
g_param_spec_boolean ("stats", "Stats",
"Enable clock stats (unimplemented)", DEFAULT_STATS,
G_PARAM_READWRITE));
g_object_class_install_property (G_OBJECT_CLASS (klass), PROP_WINDOW_SIZE,
g_param_spec_int ("window-size", "Window size",
"The size of the window used to calculate rate and offset", 2, 1024,
......@@ -1114,6 +1115,7 @@ do_linear_regression (GstClock * clock, GstClockTime * m_num,
invalid:
{
GST_DEBUG_OBJECT (clock, "sxx == 0, regression failed");
return FALSE;
}
}
......@@ -1130,6 +1132,12 @@ invalid:
* are available, a linear regression algorithm is run on the
* observations and @clock is recalibrated.
*
* If this functions returns %TRUE, @r_squared will contain the sum
* of squared distances from the interpolated values. Lower values
* means the regression yielded a more accurate result. This value can
* be used to control the sampling frequency of the master and slave
* clocks.
*
* Returns: TRUE if enough observations were added to run the
* regression algorithm.
*
......
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