lookup

I have the following lookup table: lkp <- data.frame( x=c(0,0.2,0.65,0.658,1.3,1.76,2.7), y=c(1,1,1,0.942,0.942, 0.92, 0.89) ) I would like to get the value of Y of a given X value. If the X value exists in the table then the exact Y of the table should be returned. If the X value does not exist, then the Y value should be returned as linear interpolation of the 2 nearest neighbors (only the 2 nearest neighbors). I would not like to fit a model to the overall data. for the above table for X=0.2 Y=1 (exact lookup) for X=2 Y=0.91 (linear interpolation between the last 2 rows of the data frame)

This question already has an answer here: LINQ Convert Dictionary to Lookup 2 answers I have a Dictionary that has a signature: Dictionary<int, List<string>> . I'd like to convert it to a Lookup with a signature: Lookup<int, string> . I tried: Lookup<int, string> loginGroups = mapADToRole.ToLookup(ad => ad.Value, ad => ad.Key); But that is not working so well. You could use: var lookup = dictionary.SelectMany(p => p.Value .Select(x => new { p.Key, Value = x})) .ToLookup(pair => pair.Key, pair => pair.Value); (You could use KeyValuePair instead of an anonymous type - I mostly didn't for

Where can I find information on the differences between calling on a column within a data.frame via: df <- data.frame(x=1:20,y=letters[1:20],z=20:1) df$x df["x"] They both return the "same" results, but not necessarily in the same format. Another thing that I've noticed is that df$x returns a list. Whereas df["x"] returns a data.frame. EDIT: However, knowing which one to use in which situation has become a challenge. Is there a best practice here or does it really come down to knowing what the command or function requires? So far I've just been cycling through them if my function doesn't work

Is there any way to perform a dictionary lookup based on a String tensor in Tensorflow? In plain Python, I'd do something like value = dictionary[key] . Now I'd like to do the same thing at Tensorflow runtime, when I have my key as a String tensor. Something like value_tensor = tf.dict_lookup(string_tensor) would be nice. You might find tensorflow.contrib.lookup helpful: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lookup/lookup_ops.py https://www.tensorflow.org/api_docs/python/tf/contrib/lookup/HashTable In particular, you can do: table = tf.contrib.lookup.HashTable

How should this code behave? It calls generic function ignoring my overload if I use qualified name in call_read() function; and it calls overload first and then generic version if I use unqualified name. What's the difference? Is it a bug in GCC? #include <iostream> struct info1 {}; struct info2 {}; template<class T> void read(T& x) { std::cout << "generic" << std::endl; } template<class T> void call_read(T& x) { ::read(x); // if I replace ::read(x) with read(x) the overload is called } void read(info1& x) { std::cout << "overload" << std::endl; } int main() { info1 x; info2 y; call_read(x);