From 5a712052d87bf6e506a755b3a54252d5191bd13a Mon Sep 17 00:00:00 2001 From: kyleabeauchamp Date: Fri, 27 Feb 2015 15:10:00 -0500 Subject: [PATCH] Updates to slides --- base.html | 15 ++++++++-- index.html | 84 +++++++++++++++++++++++++--------------------------- js/slides.js | 23 ++++++++++++-- slides.md | 63 +++++++++++++++------------------------ 4 files changed, 98 insertions(+), 87 deletions(-) diff --git a/base.html b/base.html index b017f8a..5b3dcee 100644 --- a/base.html +++ b/base.html @@ -26,7 +26,8 @@ - + + - + +
+ \[ + \definecolor{data}{RGB}{18,110,213} + \definecolor{unknown}{RGB}{217,86,16} + \definecolor{learned}{RGB}{175,114,176} + \] +
diff --git a/index.html b/index.html index d6bd354..d304e8c 100644 --- a/index.html +++ b/index.html @@ -13,7 +13,7 @@ - Conformational Dynamics in Mixtape + Conformational Dynamics in MSMBuilder3 @@ -26,7 +26,8 @@ - + + - + +
+ \[ + \definecolor{data}{RGB}{18,110,213} + \definecolor{unknown}{RGB}{217,86,16} + \definecolor{learned}{RGB}{175,114,176} + \] +
@@ -52,9 +63,9 @@
-

Conformational Dynamics in Mixtape

+

Conformational Dynamics in MSMBuilder3

-

Kyle A. Beauchamp
September 3, 2014

+

Kyle A. Beauchamp
Updated Feb. 27, 2015 (msmbuilder v3.1)

 @@ -140,7 +151,7 @@

-

Mixtape: Philosophy

+

MSMBuilder: Philosophy

Let's build on scikit-learn idioms:

@@ -162,14 +173,14 @@ 

 
->>> import mixtape.cluster
->>> clusterer = mixtape.cluster.KMeans(n_clusters=4)
+>>> import msmbuilder.cluster
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=4)
 
->>> import mixtape.tica
->>> tica = mixtape.tica.tICA(n_components=3)
+>>> import msmbuilder.decomposition
+>>> tica = msmbuilder.decomposition.tICA(n_components=3)
 
->>> import mixtape.markovstatemodel
->>> msm = mixtape.markovstatemodel.MarkovStateModel()
+>>> import msmbuilder.msm
+>>> msm = msmbuilder.msm.MarkovStateModel()
@@ -188,11 +199,11 @@ 

 
->>> import mixtape.cluster
+>>> import msmbuilder.cluster
 
 >>> trajectories = [np.random.normal(size=(100, 3))]
 
->>> clusterer = mixtape.cluster.KMeans(n_clusters=4, n_init=10)
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=4, n_init=10)
 >>> clusterer.fit(trajectories)
 
 >>> clusterer.cluster_centers_
@@ -217,11 +228,11 @@ 

     
     
 
->>> import mixtape.markovstatemodel
+>>> import msmbuilder.msm
 
 >>> trajectories = [np.array([0, 0, 0, 1, 1, 1, 0, 0])]
 
->>> msm = mixtape.markovstatemodel.MarkovStateModel()
+>>> msm = msmbuilder.msm.MarkovStateModel()
 >>> msm.fit(trajectories)
 
 >>> msm.transmat_
@@ -243,11 +254,11 @@ 

     
     
 
->>> import mixtape.cluster
+>>> import msmbuilder.cluster
 
 >>> trajectories = [np.random.normal(size=(100, 3))]
 
->>> clusterer = mixtape.cluster.KMeans(n_clusters=4, n_init=10)
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=4, n_init=10)
 >>> clusterer.fit(trajectories)
 >>> Y = clusterer.transform(trajectories)
 
@@ -271,13 +282,13 @@ 

     
     
 
->>> import mixtape.cluster, mixtape.markovstatemodel
+>>> import msmbuilder.cluster, msmbuilder.msm
 >>> from sklearn.pipeline import Pipeline
 
 >>> trajectories = [np.random.normal(size=(100, 3))]
 
->>> clusterer = mixtape.cluster.KMeans(n_clusters=2, n_init=10)
->>> msm = mixtape.markovstatemodel.MarkovStateModel()
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=2, n_init=10)
+>>> msm = msmbuilder.msm.MarkovStateModel()
 >>> pipeline = Pipeline([("clusterer", clusterer), ("msm", msm)])
 
 >>> pipeline.fit(trajectories)
@@ -305,8 +316,8 @@ 

 
 
 
->>> from mixtape.featurizer import DihedralFeaturizer
->>> from mixtape.datasets import fetch_alanine_dipeptide
+>>> from msmbuilder.featurizer import DihedralFeaturizer
+>>> from msmbuilder.example_datasets import fetch_alanine_dipeptide
 >>> from matplotlib.pyplot import hexbin, plot
 
 >>> trajectories = fetch_alanine_dipeptide()["trajectories"]
@@ -349,10 +360,10 @@ 

     
 
 >>> import mdtraj as md
->>> from mixtape.featurizer import DihedralFeaturizer
->>> from mixtape.datasets import fetch_alanine_dipeptide
->>> from mixtape.cluster import KCenters
->>> from mixtape.markovstatemodel import MarkovStateModel
+>>> from msmbuilder.featurizer import DihedralFeaturizer
+>>> from msmbuilder.example_datasets import fetch_alanine_dipeptide
+>>> from msmbuilder.cluster import KCenters
+>>> from msmbuilder.msm import MarkovStateModel
 >>> from sklearn.pipeline import Pipeline
 
 >>> trajectories = fetch_alanine_dipeptide()["trajectories"]
@@ -412,27 +423,14 @@ 

  
 
 
-
-  
-    

-      
Model Scoring with GMRQ

-      
We can improve a lot over KMeans.

-    

-    

-
-
-

- 
-
-
 
 
   
   

-    
 Thanks!

-    
 Contributors: Robert M., Kyle B., Bharath R., Matt H., Steve K., Gert K., Muneeb S.

+    

+    

   

-  
 www mixtape docs
 github mixtape

+  

   

 
 
diff --git a/js/slides.js b/js/slides.js
index e633d58..9446329 100644
--- a/js/slides.js
+++ b/js/slides.js
@@ -1,6 +1,23 @@
-require(['order!modernizr.custom.45394',
-         'order!prettify/prettify', 'order!hammer', 'order!slide-controller',
-         'order!slide-deck'], function(someModule) {
+requirejs.config({
+    baseUrl: "js/",
+    paths: {
+	modernizer: "modernizr.custom.45394",
+	prettify: "https://cdnjs.cloudflare.com/ajax/libs/prettify/r298/prettify.min",
+	underscore: "https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.7.0/underscore-min",
+	hammer: "https://cdnjs.cloudflare.com/ajax/libs/hammer.js/0.6.4/hammer.min"
+    },
+    shim:  {
+	'slide-deck' : {
+	    deps: ['modernizer', 'hammer', 'underscore'],
+	    exports: 'SlideDeck',
+	},
+	'modernizer' : 'Modernizr',
+	'hammer': {exports: 'Hammer'},
+    }	
+});
+
+require(['modernizer', 'prettify', 'hammer', 'slide-controller',
+	 'slide-deck'],function(someModule) {
 
 var initElasticity = function() {    
     var resize = function() {
diff --git a/slides.md b/slides.md
index 8b79c92..e45e152 100644
--- a/slides.md
+++ b/slides.md
@@ -1,10 +1,6 @@
-% title: Conformational Dynamics in Mixtape
+% title: Conformational Dynamics in MSMBuilder3
 % author: Kyle A. Beauchamp
-% author: September 3, 2014
-% thankyou: Thanks!
-% thankyou_details: Contributors: Robert M., Kyle B., Bharath R., Matt H., Steve K., Gert K., Muneeb S.
-% contact: www mixtape docs
-% contact: github mixtape
+% author: Updated Feb. 27, 2015 (msmbuilder v3.1)
 
 ---
 title: Old-School Analysis of MD Data
@@ -63,7 +59,7 @@ title: Enter Data Science
 
 
 ---
-title: Mixtape: Philosophy
+title: MSMBuilder: Philosophy
 
 Let's build on [scikit-learn](http://scikit-learn.org/stable/) idioms:
 
@@ -80,14 +76,14 @@ title: Everything is a Model()!
 
 
 
->>> import mixtape.cluster
->>> clusterer = mixtape.cluster.KMeans(n_clusters=4)
+>>> import msmbuilder.cluster
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=4)
 
->>> import mixtape.tica
->>> tica = mixtape.tica.tICA(n_components=3)
+>>> import msmbuilder.decomposition
+>>> tica = msmbuilder.decomposition.tICA(n_components=3)
 
->>> import mixtape.markovstatemodel
->>> msm = mixtape.markovstatemodel.MarkovStateModel()
+>>> import msmbuilder.msm
+>>> msm = msmbuilder.msm.MarkovStateModel()
 
 

 
@@ -104,11 +100,11 @@ title: Models fit() data!
 
 
 
->>> import mixtape.cluster
+>>> import msmbuilder.cluster
 
 >>> trajectories = [np.random.normal(size=(100, 3))]
 
->>> clusterer = mixtape.cluster.KMeans(n_clusters=4, n_init=10)
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=4, n_init=10)
 >>> clusterer.fit(trajectories)
 
 >>> clusterer.cluster_centers_
@@ -130,11 +126,11 @@ title: fit() acts on lists of sequences
 
 
 
->>> import mixtape.markovstatemodel
+>>> import msmbuilder.msm
 
 >>> trajectories = [np.array([0, 0, 0, 1, 1, 1, 0, 0])]
 
->>> msm = mixtape.markovstatemodel.MarkovStateModel()
+>>> msm = msmbuilder.msm.MarkovStateModel()
 >>> msm.fit(trajectories)
 
 >>> msm.transmat_
@@ -152,11 +148,11 @@ title: Models transform() data!
 
 
 
->>> import mixtape.cluster
+>>> import msmbuilder.cluster
 
 >>> trajectories = [np.random.normal(size=(100, 3))]
 
->>> clusterer = mixtape.cluster.KMeans(n_clusters=4, n_init=10)
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=4, n_init=10)
 >>> clusterer.fit(trajectories)
 >>> Y = clusterer.transform(trajectories)
 
@@ -175,13 +171,13 @@ title: Pipeline() concatenates models!
 
 
 
->>> import mixtape.cluster, mixtape.markovstatemodel
+>>> import msmbuilder.cluster, msmbuilder.msm
 >>> from sklearn.pipeline import Pipeline
 
 >>> trajectories = [np.random.normal(size=(100, 3))]
 
->>> clusterer = mixtape.cluster.KMeans(n_clusters=2, n_init=10)
->>> msm = mixtape.markovstatemodel.MarkovStateModel()
+>>> clusterer = msmbuilder.cluster.KMeans(n_clusters=2, n_init=10)
+>>> msm = msmbuilder.msm.MarkovStateModel()
 >>> pipeline = Pipeline([("clusterer", clusterer), ("msm", msm)])
 
 >>> pipeline.fit(trajectories)
@@ -206,8 +202,8 @@ Featurizers wrap MDTraj functions via the `transform()` function
 
 
 
->>> from mixtape.featurizer import DihedralFeaturizer
->>> from mixtape.datasets import fetch_alanine_dipeptide
+>>> from msmbuilder.featurizer import DihedralFeaturizer
+>>> from msmbuilder.example_datasets import fetch_alanine_dipeptide
 >>> from matplotlib.pyplot import hexbin, plot
 
 >>> trajectories = fetch_alanine_dipeptide()["trajectories"]
@@ -242,10 +238,10 @@ title: Old-school MSMs
 
 
 >>> import mdtraj as md
->>> from mixtape.featurizer import DihedralFeaturizer
->>> from mixtape.datasets import fetch_alanine_dipeptide
->>> from mixtape.cluster import KCenters
->>> from mixtape.markovstatemodel import MarkovStateModel
+>>> from msmbuilder.featurizer import DihedralFeaturizer
+>>> from msmbuilder.example_datasets import fetch_alanine_dipeptide
+>>> from msmbuilder.cluster import KCenters
+>>> from msmbuilder.msm import MarkovStateModel
 >>> from sklearn.pipeline import Pipeline
 
 >>> trajectories = fetch_alanine_dipeptide()["trajectories"]
@@ -294,14 +290,3 @@ for fold, (train_index, test_index) in enumerate(cv):
 Also scikit-learn's GridSearchCV and RandomizedSearchCV.
 
 
----
-title: Model Scoring with GMRQ
-subtitle: We can improve a lot over KMeans.
-
-

-
-
-

-
-
-