camping in the Cascades

 

Over Memorial Day weekend Jack and I camped at Jack's family's property near Cle Elum, which is east of Snoqualmie Pass, in the foothills of the Cascades. 

The property has been in the family since two of Jack’s stepdad’s great-uncles claimed it for gold mining around 1920.  Nearby are several small mines which are still active and other land selectively logged by the Forest Service.

The logged sections were in much better condition, with less erosion and more undergrowth, than what we saw in France (below). 

This was my first trip to the property as well as my first time camping on private land. It seems like you can hike where you want as long as you’re respectful and you avoid strongly-worded private property signs.

We enjoyed hiking, lazing about, having plentiful firewood and spending time with our borrowed dog.  It was a little chilly but that was good for walking and the bugs weren’t out in force yet.

It’s exciting that we can drive there from downtown in less than two hours. This proximity to wilderness is one of things that makes Seattle so special.

We’ll be back!

verbs of the trip

 

 

Cari and I finally finished transcribing our trip log this morning- a fairly concise record of what we did each day of the trip.  We used the word-cloud generator wordle to generate the word-frequency diagram above.  The trip involved quite a bit of walking, did you hear?

(click on the image to enlarge)

language models

At work, I make language models.  This is a little explanation of what a language model is.

"All models are wrong, but some are useful."

I'm not sure who said this, but it's a good thing to keep in mind.

Imagine you have a language and you want to be able to predict what words are likely to be strung together.  You need to create a model, which is just a structured representation.  This model will never be perfect, because it will always be incomplete and the way people use language is constantly evolving.  Nevertheless, the idea is that if you get enough data (texts, transcripts etc.) and you represent it intelligently, you'll be able to make a useful model.

An ngram model is a model where you look at a window of n words.  The most basic model would simply count ngrams.    Lets say we want to use a bigram (a 2gram, where the window is 2 words).

We will train the model based on this is our very very short input text:
The quick fox jumped over the candlestick.  The fox then melted down the candlestick and jumped over the moon.

If we just iterate through the text using a window of two, we get the following bigrams:
the quick
quick fox
fox jumped
jumped over
over the
the candlestick
candlestick .
. the
the fox
fox then
then melted
melted down
down the
the candlestick
candlestick and
and jumped
jumped over
over the
the moon
moon .

You'll notice some of them occur more than once.  So we'll add counts to them like this:

1 the quick
1 quick fox
1 fox jumped
2 jumped over
2 over the
2 the candlestick
1 candlestick .
1 . the
1 the fox
1 fox then
1 then melted
1 melted down
1 down the
1 candlestick and
1 and jumped
1 the moon
1 moon .

This is a language model, albeit not a good one because it's not based on enough data.

Now, imagine you're looking over someone's shoulder as she types.  If she types "the", based on this model you'd predict that the next word would be "quick", "candlestick", "fox", or "moon."  Because "the candlestick" occurred twice while the other combinations only occurred once, you'd probably guess "candlestick" if you had to pick just one.  Based on this model, and the much more accurate model in your brain, you wouldn't predict "the and" or "the then" or "the the."

Now you know what an ngram is.  This is a fun Google site where you can see the frequency over time of different ngrams in models created from books.

For example:
trigram "thou art my"
trigram "never say never"