I'm trying to use the `naiveBayes()` function from the [e1071](https://cran.r-project.org/web/packages/e1071/index.html) package. When I add a non-zero `laplace` argument, my resulting probability estimates are not changing and I don't understand why.

Example:

```
library(e1071)

# Generate data
train.x <- data.frame(x1=c(1,1,0,0), x2=c(1,0,1,0))
train.y <- factor(c("cat", "cat", "dog", "dog"))
test.x <- data.frame(x1=c(1), x2=c(1))

# without laplace smoothing
classifier <- naiveBayes(x=train.x, y=train.y, laplace=0)
predict(classifier, test.x, type="raw") # returns (1, 0.00002507)

# with laplace smoothing
classifier <- naiveBayes(x=train.x, y=train.y, laplace=1)
predict(classifier, test.x, type="raw") # returns (1, 0.00002507)

```

I would expect the probabilities to change in this case since all the training instances for the "dog" class have 0 for **x1**. To check this, here's the same thing using Python

Python example:

```
import numpy as np
from sklearn.naive_bayes import BernoulliNB

train_x = pd.DataFrame({'x1':[1,1,0,0], 'x2':[1,0,1,0]})
train_y = np.array(["cat", "cat", "dog", "dog"])
test_x = pd.DataFrame({'x1':[1,], 'x2':[1,]})

# alpha (i.e. laplace = 0)
classifier = BernoulliNB(alpha=.00000001)
classifier.fit(X=train_x, y=train_y)
classifier.predict_proba(X=test_x) # returns (1, 0)

# alpha (i.e. laplace = 1)
classifier = BernoulliNB(alpha=1)
classifier.fit(X=train_x, y=train_y)
classifier.predict_proba(X=test_x) # returns (.75, .25)

```

Why am I getting this unexpected result using e1071?