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# Predict the test set using the model | |
pred_lasso = predict(glmmod, test_sparse, type="response", s=best.lambda) | |
pred_lasso | |
# Apply a threshold | |
new_pred_lasso = ifelse(pred_lasso >= 0.5, 1, 0) | |
new_pred_lasso = data.frame(new_pred_lasso) | |
data_lasso = cbind(test[,2], new_pred_lasso) | |
names(data_lasso) = c("actual", "pred") | |
xtab_lasso = table(data_lasso$actual, data_lasso$pred) |
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# getBicop is a snippet from https://stats.stackexchange.com/questions/15011/generate-a-random-variable-with-a-defined-correlation-to-an-existing-variables/15035#15035 | |
# returns a data frame of two variables which correlate with a population correlation of rho | |
# If desired, one of both variables can be fixed to an existing variable by specifying x | |
getBiCop <- function(n, rho, mar.fun=rnorm, x = NULL, ...) { | |
if (!is.null(x)) {X1 <- x} else {X1 <- mar.fun(n, ...)} | |
if (!is.null(x) & length(x) != n) warning("Variable x does not have the same length as n!") | |
C <- matrix(rho, nrow = 2, ncol = 2) | |
diag(C) <- 1 | |
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import gensim | |
from gensim.models.keyedvectors import KeyedVectors | |
# Load Google's pre-trained Word2Vec model. | |
model = KeyedVectors.load_word2vec_format('I:/Downloads/GoogleNews-vectors-negative300.bin/GoogleNews-vectors-negative300.bin', binary=True) | |
word1 = 'anger' | |
word2 = 'trust' | |
model.most_similar(positive = [word1, word2]) | |
word_w2v = 'resentment' |
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import gensim | |
from gensim.models.keyedvectors import KeyedVectors | |
# Load Google's pre-trained Word2Vec model. | |
model = KeyedVectors.load_word2vec_format('I:/Downloads/GoogleNews-vectors-negative300.bin/GoogleNews-vectors-negative300.bin', binary=True) | |
word1 = 'anger' | |