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Jaro Similarity


Jaro Similarity

By : Nucleo
Date : November 29 2020, 09:01 AM
Any of those help Transpositions in this context are all those characters that don't match the same position on strings
from wikipedia
code :
m = 10
t = 4/2 = 2
|S1| = 10
|S2| = 10
d = 1/3 * (10/10 + 10/10 + (10-2)/10) = 0.933


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Spark Item Similarity Interpretation (Cross-Similarity and Similarity)

Spark Item Similarity Interpretation (Cross-Similarity and Similarity)


By : Hans Hansen
Date : March 29 2020, 07:55 AM
I wish this help you In both cases the matrix is telling you that the item-id key is similar to the listed items by the LLR value attached to each similar item. Similar in the sense that similar users purchased the items. In the second case it is saying that similar people viewed the items and this view also appears to have led of a purchase of the same item.
Cooccurrence works for purchases alone, cross-occurrence adds the check to make sure the view also correlated with a purchase. This allows you to use both for recommendations.
word2vec_basic output: trying to test word similarity versus human similarity scores

word2vec_basic output: trying to test word similarity versus human similarity scores


By : pavan teja
Date : March 29 2020, 07:55 AM
it should still fix some issue To answer my own question: Yes, the results are dismal, but that's because the model is too small and is trained on too little data. As simple as that. The implementation I experimented with uses a corpus of 17M words and runs for 100K steps, and takes just 2 adjacent words of context for an embedding size of 128. I got a larger Wikipedia sample with 124M words, increased context to 24 words (12 on each side), embedding size to 256, and trained for 1.8M steps, and voila! The correlation (as measured in my question above) grew to 0.24.
I then implemented subsampling of frequent words as described in this tutorial and correlation jumped further to 0.33. Finally I left my laptop overnight to train with 36 words of context and 3.2M steps, and it got all the way to 0.42! I think we can call this success.
cosine similarity(patient similarity metric) between 48k patients data with predictive variables

cosine similarity(patient similarity metric) between 48k patients data with predictive variables


By : user6951129
Date : March 29 2020, 07:55 AM
it helps some times First things first: You can find more rigorous treatments of cosine similarity at either of these posts:
Find cosine similarity between two arrays Creating co-occurrence matrix
code :
diasbp_rage <- diasbp_max - diasbp_min
library(lsa)
library(reshape2)

psm_sample <- read.csv("psm_sample.csv")

row.names(psm_sample) <-
  make.names(paste0("patid.", as.character(psm_sample$subject_id)), unique = TRUE)
temp <- sapply(psm_sample, class)
temp <- cbind.data.frame(names(temp), as.character(temp))
names(temp) <- c("variable", "possible.type")

numeric.cols <- (temp$possible.type %in% c("factor", "integer") &
                   (!(grepl(
                     pattern = "_id$", x = temp$variable
                   ))) &
                   (!(
                     grepl(pattern = "_code$", x = temp$variable)
                   )) &
                   (!(
                     grepl(pattern = "_type$", x = temp$variable)
                   ))) | temp$possible.type == "numeric"

psm_numerics <- psm_sample[, numeric.cols]
row.names(psm_numerics) <- row.names(psm_sample)

psm_numerics$gender <- as.integer(psm_numerics$gender)

psm_scaled <- scale(psm_numerics)

pair.these.up <- psm_scaled
# checking for independence of variables
# if the following PDF pair plot is too big for your computer to open,
# try pair-plotting some random subset of columns
# keep.frac <- 0.5
# keep.flag <- runif(ncol(psm_scaled)) < keep.frac
# pair.these.up <- psm_scaled[, keep.flag]
# pdf device sizes are in inches
dev <-
  pdf(
    file = "psm_pairs.pdf",
    width = 50,
    height = 50,
    paper = "special"
  )
pairs(pair.these.up)
dev.off()

#transpose the dataframe to get the
#similarity between patients
cs <- lsa::cosine(t(psm_scaled))

# this is super inefficnet, because cs contains
# two identical triangular matrices
cs.melt <- melt(cs)
cs.melt <- as.data.frame(cs.melt)
names(cs.melt) <- c("enc.A", "enc.B", "similarity")

extract.pat <- function(enc.col) {
  my.patients <-
    sapply(enc.col, function(one.pat) {
      temp <- (strsplit(as.character(one.pat), ".", fixed = TRUE))
      return(temp[[1]][[2]])
    })
  return(my.patients)
}
cs.melt$pat.A <- extract.pat(cs.melt$enc.A)
cs.melt$pat.B <- extract.pat(cs.melt$enc.B)

same.pat <-      cs.melt[cs.melt$pat.A == cs.melt$pat.B ,]
different.pat <- cs.melt[cs.melt$pat.A != cs.melt$pat.B ,]

most.dissimilar <-
  different.pat[which.min(different.pat$similarity),]

dissimilar.pat.frame <- rbind(psm_numerics[rownames(psm_numerics) ==
                                             as.character(most.dissimilar$enc.A) ,],
                              psm_numerics[rownames(psm_numerics) ==
                                             as.character(most.dissimilar$enc.B) ,])

print(t(dissimilar.pat.frame))
                  patid.68.49   patid.9
gender                1.00000   2.00000
age                  41.85000  41.79000
sysbp_min            72.00000 106.00000
sysbp_max            95.00000 217.00000
diasbp_min           42.00000  53.00000
diasbp_max           61.00000 107.00000
meanbp_min           52.00000  67.00000
meanbp_max           72.00000 132.00000
resprate_min         20.00000  14.00000
resprate_max         35.00000  19.00000
tempc_min            36.00000  35.50000
tempc_max            37.55555  37.88889
spo2_min             90.00000  95.00000
spo2_max            100.00000 100.00000
bicarbonate_min      22.00000  26.00000
bicarbonate_max      22.00000  30.00000
creatinine_min        2.50000   1.20000
creatinine_max        2.50000   1.40000
glucose_min          82.00000 129.00000
glucose_max          82.00000 178.00000
hematocrit_min       28.10000  37.40000
hematocrit_max       28.10000  45.20000
potassium_min         5.50000   2.80000
potassium_max         5.50000   3.00000
sodium_min          138.00000 136.00000
sodium_max          138.00000 140.00000
bun_min              28.00000  16.00000
bun_max              28.00000  17.00000
wbc_min               2.50000   7.50000
wbc_max               2.50000  13.70000
mingcs               15.00000  15.00000
gcsmotor              6.00000   5.00000
gcsverbal             5.00000   0.00000
gcseyes               4.00000   1.00000
endotrachflag         0.00000   1.00000
urineoutput        1674.00000 887.00000
vasopressor           0.00000   0.00000
vent                  0.00000   1.00000
los_hospital         19.09310   4.88130
los_icu               3.53680   5.32310
sofa                  3.00000   5.00000
saps                 17.00000  18.00000
posthospmort30day     1.00000   0.00000
Search the similarity of 2 strings in java using part of word matching, not cosine similarity

Search the similarity of 2 strings in java using part of word matching, not cosine similarity


By : user3480692
Date : March 29 2020, 07:55 AM
hop of those help? For each search string, split it into words using haystack.split("\\s+") (\\s+ is regexp-ese for 'the strings are separated by whitespace').
Then, to obtain a 'score' you need 2 numbers: How many words matched, and how many words there are total. You sort descending on first, and ascending on last, which gets you the behaviour you seem to want.
code :
String[] needle = "super cold white snow".split("\\s+");
String[] haystack = "white image superdupercold".split("\\s+");
int matchedWords = 0, totalWords = haystack.length;
for (String n : needle) {
    boolean found = false;
    for (String hay : haystack) {
        if (hay.contains(n)) {
            found = true;
            break;
        }
    }
    if (found) matchedWords++;
}
private static final long MULTIPLIER = 0x100000000L;
long score = MULTIPLIER * matchedWords + (Integer.MAX_VALUE - totalWords);
@Value
class Result { String needle; int words, total; }

list.sort(
    Comparator.comparing(Result::getWords).reversed().
    thenComparing(Comparator.comparing(Result::getTotal));

list.stream().map(Result::getNeedle).forEach(System.out::println);
How to estimate 2D similarity transformation (linear conformal, nonreflective similarity) in OpenCV?

How to estimate 2D similarity transformation (linear conformal, nonreflective similarity) in OpenCV?


By : vaibhav
Date : March 29 2020, 07:55 AM
should help you out You can use estimateRigidTransform (I do not know whether it is RANSAC, the code at http://code.opencv.org/projects/opencv/repository/revisions/2.4.4/entry/modules/video/src/lkpyramid.cpp says RANSAC in its comment), the third parameter is set to false in order to get just scale+rotation+translation:
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