Essentially what I am trying to do is boost every document by a certain
factor, so that
the boost is between 1.0 and 2.0. After this, I we are trying to do a search
across multiple fields
and have a computation based purely on tf. Example -
if (field1)
tf = some function
else if (field2)
tf =
Thanks Hoss. Suppose, I go ahead and modify Similarity.java from
static {
for (int i = 0; i < 256; i++)
NORM_TABLE[i] = SmallFloat.byte315ToFloat((byte)i);
}
TO
static {
for (int i = 0; i < 256; i++)
NORM_TABLE[i] = (float) i * 100.0 /256.0;
}
Should this work ?
Thanks
I want to modify the norms to only include values between 0 and 100.
Currently, I have a custom implementation of the default similarity. Is it
sufficient to override the encodeNorm and decodeNorm methods from the base
implementation in my custom Similarity class ? Please let me know if there
are
I am trying to use the build from Jan. 5, 2007 to incorporate with Solr (
compiled using javac 1.5.0_02 on Solaris). When I try to add a new document
to the index, I always get a class def found error.
Posting file vidcard.xml to http://localhost:8983/solr/update
java.lang.NoClassDefFoundError:
Hi,
I am trying to create a linear function to influence the similarity
computation. For example -
if tf = 4, f(tf) = 150 * 1 + 150 * 0.3
= 195
The first occurrence is multiplied by 150. The next three occurrences are
mulitplied by 150 and
divided by 10 (3/10).
Ho
Hi,
I am trying to create a linear function to influence the similarity
computation. For example -
if tf = 4, f(tf) = 150 * 1 + 150 * 0.3
= 195
The first occurrence is multiplied by 150. The next three occurrences are
mulitplied by 150 and
divided by 10 (3/10).
Ho
Hi,
I am trying to create a linear function to influence the similarity
computation. For example -
if tf = 4, f(tf) = 150 * 1 + 150 * 0.3
= 195
The first occurrence is multiplied by 150. The next three occurrences are
mulitplied by 150 and
divided by 10 (3/10).
However, w
I am trying to build a scoring function which is additive across multiple
fields that are searched.
For instance, if a user searches for "Web PHP", I want the search to happen
over fld1, fld2
and then compute the score as,
score = similarity score(fld1) + similarity score(fld2) +
I think I ha