From d0485e48486ca06b4de922075fe63265c7bd5e7e Mon Sep 17 00:00:00 2001 From: richermans Date: Fri, 5 Feb 2016 16:30:33 +0800 Subject: [PATCH] Additional changes in the README --- README.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index e457823..02e1eae 100644 --- a/README.md +++ b/README.md @@ -65,9 +65,7 @@ PLDA fit does also accept two extra arguments: ```python #Transform the features first to a given target dimension. Default is keeping the dimension targetdim=10 -#Smoothing factor does increase the performance. Its a value between 0 and 1. Does affect the covariance matrix -smoothing=0.5 -plda.fit(X,Y,targetdim,smoothing) +plda.fit(X,Y,targetdim) ``` LDA can then after fitting be used to directly score any incoming utterance using predict_log_prob(SAMPLE) @@ -84,11 +82,18 @@ Generally it is recommended to have an held out set to do this estimation. The n ```python ENROL_X=np.random.rand(n_samples,featdim) ENROL_Y=np.arange(n_samples,dtype='uint') -transformed_vectors = plda.transform(ENROL_X,ENROL_Y) +#Smoothing factor does increase the performance. Its a value between 0 and 1. #Does affect the covariance matrix. Optional! +smoothing=0.5 + +#Transform the features first to a given target dimension. Default is keeping the dimension +targetdim=10 + +transformed_vectors = plda.transform(ENROL_X,ENROL_Y,targetdim,smoothing) Otherdata = np.random.rand(m_samples,featdim) plda.norm(Otherdata,transformed_vectors) ``` +Note that if targetdim is given, all future plda.transform() calls also need targetdim as a valid parameter And finally one can score any model against a utterance by: