README.md in anomaly-0.0.2 vs README.md in anomaly-0.0.3

@@ -14,55 +14,62 @@
 
 ```sh
 bundle install
 ```
 
-For max performance (about 3x faster), also install the NArray gem:
+For max performance (~ 2x faster), also install the NArray gem:
 
 ```ruby
 gem "narray"
 ```
 
 Anomaly will automatically detect it and use it.
 
 ## How to Use
 
-Train the detector with **only non-anomalies**. Each row is a sample.
+Say we have weather data for sunny days and we're trying to detect days that aren't sunny. The data looks like:
 
 ```ruby
-train_data = [
-  [0.1, 100, 1.4],
-  [0.2, 101, 2.1],
-  [0.5, 102, 1.6]
+# Each row is a different day.
+# [temperature (°F), humidity (%), pressure (in)]
+weather_data = [
+  [85, 68, 10.4],
+  [88, 62, 12.1],
+  [86, 64, 13.6],
+  ...
 ]
-ad = Anomaly::Detector.new(train_data)
 ```
 
+Train the detector with **only non-anomalies** (sunny days in our case).
+
+```ruby
+ad = Anomaly::Detector.new(weather_data)
+```
+
 That's it! Let's test for anomalies.
 
 ```ruby
-test_sample = [1.0, 100, 1.4]
+# 79°F, 66% humidity, 12.3 in. pressure
+test_sample = [79, 66, 12.3]
 ad.probability(test_sample)
-# => 0.0007328491480297603
+# => 7.537174740907633e-08
 ```
 
 **Super-important:** You must select a threshold for anomalies (which we denote with ε - "epsilon")
 
 Probabilities less than ε are considered anomalies. If ε is higher, more things are considered anomalies.
 
 ``` ruby
 ad.anomaly?(test_sample, 1e-10)
 # => false
-ad.anomaly?(test_sample, 0.5)
+ad.anomaly?(test_sample, 1e-5)
 # => true
 ```
 
-Here's sample to code to help you find the best ε for your application.
+The wiki has [sample code](https://github.com/ankane/anomaly/wiki/Home) to help you find the best ε for your application.
 
-```ruby
-# TODO
-```
+### Persistence
 
 You can easily persist the detector to a file or database - it's very tiny.
 
 ```ruby
 serialized_ad = Marshal.dump(ad)
@@ -73,9 +80,14 @@
 # ...
 
 # Read it later
 ad2 = Marshal.load(File.open("anomaly_detector.dump", "r").read)
 ```
+
+## TODO
+
+- Train in chunks (for very large datasets)
+- Multivariate normal distribution (possibly)
 
 ## Contributing
 
 1. Fork it
 2. Create your feature branch (`git checkout -b my-new-feature`)