Computing cosine.similarity in R gives different results compared to manual?
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Here are my vectors:
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 0.688 0.317
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 2.94 0.324
or for simplicity:
a <- c(.688,.317)
b <- c(2.94, .324)
I want to compute tcR::cosine.similarity:
cosine.similarity(a,b, .do.norm = T) gives me 1.388816
If I will do it myself according to Wikipedia:
sum(c(.688,.317) * c(2.94, .324)) / (sqrt(sum(c(.688,.317) ^ 2)) * sqrt(sum(c(2.94, .324) ^ 2)))
And I get 0.948604 so what is different here?
Please advise. I suppose it is the normalization but will be happy for your help.
r cosine-similarity
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
add a comment |
Here are my vectors:
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 0.688 0.317
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 2.94 0.324
or for simplicity:
a <- c(.688,.317)
b <- c(2.94, .324)
I want to compute tcR::cosine.similarity:
cosine.similarity(a,b, .do.norm = T) gives me 1.388816
If I will do it myself according to Wikipedia:
sum(c(.688,.317) * c(2.94, .324)) / (sqrt(sum(c(.688,.317) ^ 2)) * sqrt(sum(c(2.94, .324) ^ 2)))
And I get 0.948604 so what is different here?
Please advise. I suppose it is the normalization but will be happy for your help.
r cosine-similarity
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
add a comment |
Here are my vectors:
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 0.688 0.317
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 2.94 0.324
or for simplicity:
a <- c(.688,.317)
b <- c(2.94, .324)
I want to compute tcR::cosine.similarity:
cosine.similarity(a,b, .do.norm = T) gives me 1.388816
If I will do it myself according to Wikipedia:
sum(c(.688,.317) * c(2.94, .324)) / (sqrt(sum(c(.688,.317) ^ 2)) * sqrt(sum(c(2.94, .324) ^ 2)))
And I get 0.948604 so what is different here?
Please advise. I suppose it is the normalization but will be happy for your help.
r cosine-similarity
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
Here are my vectors:
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 0.688 0.317
lin_acc_mag_mean vel_ang_unc_mag_mean
<dbl> <dbl>
1 2.94 0.324
or for simplicity:
a <- c(.688,.317)
b <- c(2.94, .324)
I want to compute tcR::cosine.similarity:
cosine.similarity(a,b, .do.norm = T) gives me 1.388816
If I will do it myself according to Wikipedia:
sum(c(.688,.317) * c(2.94, .324)) / (sqrt(sum(c(.688,.317) ^ 2)) * sqrt(sum(c(2.94, .324) ^ 2)))
And I get 0.948604 so what is different here?
Please advise. I suppose it is the normalization but will be happy for your help.
r cosine-similarity
r cosine-similarity
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
asked Nov 26 '18 at 15:42
SteveSSteveS
666311
666311
add a comment |
add a comment |
1 Answer
1
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oldest
votes
In the tcR package the cosine.similarity function contains the following:
function (.alpha, .beta, .do.norm = NA, .laplace = 0)
{
.alpha <- check.distribution(.alpha, .do.norm, .laplace)
.beta <- check.distribution(.beta, .do.norm, .laplace)
sum(.alpha * .beta)/(sum(.alpha^2) * sum(.beta^2))
}
The intervening check.distribution calculation returns a vector that sums to 1, but does not appear to be normalized.
I'd recommend using the cosine function in the lsa package, instead. This one produces the correct value. It also permits calculation of the cosine similarity for a whole matrix of vectors organized in columns. For example, cosine(cbind(a,b,b,a)) yields the following:
a b b a
a 1.000000 0.948604 0.948604 1.000000
b 0.948604 1.000000 1.000000 0.948604
b 0.948604 1.000000 1.000000 0.948604
a 1.000000 0.948604 0.948604 1.000000
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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In the tcR package the cosine.similarity function contains the following:
function (.alpha, .beta, .do.norm = NA, .laplace = 0)
{
.alpha <- check.distribution(.alpha, .do.norm, .laplace)
.beta <- check.distribution(.beta, .do.norm, .laplace)
sum(.alpha * .beta)/(sum(.alpha^2) * sum(.beta^2))
}
The intervening check.distribution calculation returns a vector that sums to 1, but does not appear to be normalized.
I'd recommend using the cosine function in the lsa package, instead. This one produces the correct value. It also permits calculation of the cosine similarity for a whole matrix of vectors organized in columns. For example, cosine(cbind(a,b,b,a)) yields the following:
a b b a
a 1.000000 0.948604 0.948604 1.000000
b 0.948604 1.000000 1.000000 0.948604
b 0.948604 1.000000 1.000000 0.948604
a 1.000000 0.948604 0.948604 1.000000
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
add a comment |
In the tcR package the cosine.similarity function contains the following:
function (.alpha, .beta, .do.norm = NA, .laplace = 0)
{
.alpha <- check.distribution(.alpha, .do.norm, .laplace)
.beta <- check.distribution(.beta, .do.norm, .laplace)
sum(.alpha * .beta)/(sum(.alpha^2) * sum(.beta^2))
}
The intervening check.distribution calculation returns a vector that sums to 1, but does not appear to be normalized.
I'd recommend using the cosine function in the lsa package, instead. This one produces the correct value. It also permits calculation of the cosine similarity for a whole matrix of vectors organized in columns. For example, cosine(cbind(a,b,b,a)) yields the following:
a b b a
a 1.000000 0.948604 0.948604 1.000000
b 0.948604 1.000000 1.000000 0.948604
b 0.948604 1.000000 1.000000 0.948604
a 1.000000 0.948604 0.948604 1.000000
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
add a comment |
In the tcR package the cosine.similarity function contains the following:
function (.alpha, .beta, .do.norm = NA, .laplace = 0)
{
.alpha <- check.distribution(.alpha, .do.norm, .laplace)
.beta <- check.distribution(.beta, .do.norm, .laplace)
sum(.alpha * .beta)/(sum(.alpha^2) * sum(.beta^2))
}
The intervening check.distribution calculation returns a vector that sums to 1, but does not appear to be normalized.
I'd recommend using the cosine function in the lsa package, instead. This one produces the correct value. It also permits calculation of the cosine similarity for a whole matrix of vectors organized in columns. For example, cosine(cbind(a,b,b,a)) yields the following:
a b b a
a 1.000000 0.948604 0.948604 1.000000
b 0.948604 1.000000 1.000000 0.948604
b 0.948604 1.000000 1.000000 0.948604
a 1.000000 0.948604 0.948604 1.000000
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
In the tcR package the cosine.similarity function contains the following:
function (.alpha, .beta, .do.norm = NA, .laplace = 0)
{
.alpha <- check.distribution(.alpha, .do.norm, .laplace)
.beta <- check.distribution(.beta, .do.norm, .laplace)
sum(.alpha * .beta)/(sum(.alpha^2) * sum(.beta^2))
}
The intervening check.distribution calculation returns a vector that sums to 1, but does not appear to be normalized.
I'd recommend using the cosine function in the lsa package, instead. This one produces the correct value. It also permits calculation of the cosine similarity for a whole matrix of vectors organized in columns. For example, cosine(cbind(a,b,b,a)) yields the following:
a b b a
a 1.000000 0.948604 0.948604 1.000000
b 0.948604 1.000000 1.000000 0.948604
b 0.948604 1.000000 1.000000 0.948604
a 1.000000 0.948604 0.948604 1.000000
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
answered Nov 26 '18 at 19:48
Edward CarneyEdward Carney
1,15456
1,15456
add a comment |
add a comment |
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