minor updates (clear outputs since we're using prefilled notebook versions of episodes)

Author: qualiaMachine

episodes/04-vectorSpace.md

@@ -69,13 +69,6 @@ corpus = np.array([[1,10],[8,8],[2,2],[2,2]])
 print(corpus)
 ```
 
-```txt
-[[ 1 10]
-  [ 8  8]
-  [ 2  2]
-  [ 2  2]]
-```
-
 ### Graphing our model
 
 We don't just have to think of our words as columns. We can also think of them as dimensions, and the values as coordinates for each document.
@@ -87,11 +80,6 @@ corpusT = np.transpose(corpus)
 print(corpusT)
 ```
 
-```txt
-[[ 1  8  2  2]
-  [10  8  2  2]]
-```
-
 ```python
 X = corpusT[0]
 Y = corpusT[1]
@@ -154,10 +142,6 @@ origin = np.zeros([1,4])
 print(origin)
 ```
 
-```txt
-[[0. 0. 0. 0.]]
-```
-
 ```python
 # draw our vectors
 plt.quiver(origin, origin, X, Y, color=mycolors, angles='xy', scale_units='xy', scale=1)
@@ -166,8 +150,6 @@ plt.ylim(0, 12)
 plt.show()
 ```
 
-![](fig/02-plot-vectors.png){alt='png'}
-
 Document A and document D are headed in exactly the same direction, which matches our intution that both documents are in some way similar to each other, even though they differ in length.
 
 #### Cosine Similarity
@@ -183,13 +165,6 @@ from sklearn.metrics.pairwise import cosine_similarity as cs
 cs(corpus, D)
 ```
 
-```txt
-array([[0.7739573],
-       [1.       ],
-       [1.       ],
-       [1.       ]])
-```
-
 Both A and D are considered similar by this metric. Cosine similarity is used by many models as a measure of similarity between documents and words.
 
 ### Generalizing over more dimensions
@@ -218,13 +193,6 @@ corpus = np.hstack((corpus, np.zeros((4,2))))
 print(corpus)
 ```
 
-```txt
-[[ 1. 10.  0.  0.]
-  [ 8.  8.  0.  0.]
-  [ 2.  2.  0.  0.]
-  [ 2.  2.  0.  0.]]
-```
-
 ```python
 E = np.array([[0,2,1,1]])
 F = np.array([[2,2,1,1]])
@@ -234,27 +202,13 @@ corpus = np.vstack((corpus, E))
 print(corpus)
 ```
 
-```txt
-[[ 1. 10.  0.  0.]
-  [ 8.  8.  0.  0.]
-  [ 2.  2.  0.  0.]
-  [ 2.  2.  0.  0.]
-  [ 0.  2.  1.  1.]]
-```
 
 What do you think the most similar document is to document F?
 
 ```python
 cs(corpus, F)
 ```
 
-```txt
-array([[0.69224845],
-        [0.89442719],
-        [0.89442719],
-        [0.89442719],
-        [0.77459667]])
-```
 
 This new document seems most similar to the documents B,C and D.