Abalone | Logic game for Android | Game Engine library

Here's a solution in the tidyverse. To avoid some tortuous parsing, I have left the columns with slightly different names and in a different order from what you depicted. However, the fundamental structure and content of the data is as requested.

Here is a possible approach using rrapply() in the rrapply-package to unmelt the data.frame to a nested list:

A regex is the wrong tool for comparing character counts. Any regex that satisfies this requirement is likely to be awkward and terribly inefficient. You will be far better off traversing each word and keeping track of the individual character counts.

Anyway, here is a method for constructing a regex that matches the "wrong words" (the other way around is much harder): First, from the set of distinct characters {a1,...,aN} contained in the phrase, you can match all words containing any illegal character with [^a1,...,aN]. Then, for each character c that appears n times in your target string, build a sub-expression (.*c.*){n+1}, then join these fragments with |. For clint eastwood you should get:

I think the issue might be the following: From your description of the problem you are trying to perform a regression task, i.e. predicting the age of the abalones. The age could in theory be any positive real number. Therefore, the accuracy metric you are using here is unsuited to the task, since it is used for classification tasks, that is, when the output belongs to one of a fixed and discrete set of possibilities. Therefore I would suggest using a different metric to measure your model results, such as Mean Squared Error or Mean Absolute Error, which are suitable for regression.

Also, note that while your metric (accuracy) has a value of 0, your loss function is decreasing with each epoch, which shows your model is improving :)

You can use df.append to append df1 to df2, followed by drop_duplicates, with keep='last', then sort_index and reset_index:

Updated the return Object.assign() to get the correct results:

Easy:

Try changing this line

model.fit(X_train,y_train, early_stopping_rounds=5, eval_set=([X_test,y_test]))

to

model.fit(X_train,y_train, early_stopping_rounds=5, eval_set=[(X_test,y_test)]

Your updated code which runs without error:

You have 10 attribute lines, but only 9 attributes in your data.

Also, in your data the codes are M, F, I but you have indicated they are Male, Female, Infant in your attribute statement.

I'm guessing that you think you are just assigning class to sex, but the class variable in Weka is automatically the last variable. I've rearranged this for you below and verified that it will load properly.