# some examples of list simm in action

So, we have given the code for 3 types of simm in the last post. Now, how about a nice visualisation of them in action. First, some python:
```# define square shape:
def square_fn(centre,width,height,x):
if (centre - width/2) <= x <= (centre + width/2):
return height
return 0

# define bell curve:
def bell_fn(centre,width,height,x):
return height*math.exp(- (x - centre)**2/2*width)

# define Euclidean Distance function:
def ED(f,g):
if len(f) != len(g):
print("different length vectors!")
return 0
return math.sqrt(sum((f[k] - g[k])**2 for k in range(len(f))))

# define Guassian simm:
# guassian-simm(s,f,g) = exp(-||f - g||^2/2s)
def gauss_simm(s,f,g):
return math.exp(-ED(f,g)**2/2*s)

# print out the results:
f = [square_fn(15,20,10,k) for k in range(100)]
g = [bell_fn(15,0.01,10,k) for k in range(100)]
print("Gauss simm:",round(gauss_simm(0.001,f,g),4))
print("simm:",round(list_simm(,f,g),4))
print("scaled simm:",round(rescaled_list_simm(,f,g),4))
```
Now, some examples:
f = [square_fn(15,20,10,k) for k in range(100)]
g = [bell_fn(15,0.01,10,k) for k in range(100)]
Gauss simm:0.8583
simm: 0.752
scaled simm: 0.752

f = [square_fn(15,20,10,k) for k in range(100)]
g = [bell_fn(35,0.01,10,k) for k in range(100)]
Gauss simm:0.2208
simm: 0.1698
scaled simm: 0.1698

f = [square_fn(15,20,10,k) for k in range(100)]
g = [bell_fn(75,0.01,10,k) for k in range(100)]
Gauss simm:0.1443
simm: 0.0
scaled simm: 0.0

Now some notes:
1) I used s = 0.001 in the Gaussian simm. So you may want to choose other values for that.
2) I rounded the results to 4 digits
3) In this case simm and scaled simm give essentially the same answer. This is because w*f is pretty close to w*g even without rescaling:
wf: 210
wg: 248.9

OK. Let's redo the examples with a smaller bell curve for curve g (we just drop the height from 10 to 1):
f = [square_fn(15,20,10,k) for k in range(100)]
g = [bell_fn(15,0.01,1,k) for k in range(100)]
Gauss simm: 0.4141
simm: 0.0843
scaled simm: 0.752
f = [square_fn(15,20,10,k) for k in range(100)]
g = [bell_fn(35,0.01,1,k) for k in range(100)]
Gauss simm: 0.3619
simm: 0.0203
scaled simm: 0.1698

f = [square_fn(15,20,10,k) for k in range(100)]
g = [bell_fn(75,0.01,1,k) for k in range(100)]
Gauss simm: 0.3469
simm: 0.0
scaled simm: 0.0

And now the difference between simm and scaled simm is stark. Simm is now much smaller, but scaled simm gives the exact same answer as in the previous examples (as we expect!).

And that's it for this post. I guess a superposition implementation of simm is up next!

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next: a superposition implementation of simm

updated: 19/12/2016
by Garry Morrison
email: garry -at- semantic-db.org