def fwdEngine(res_mesh, geo_factor, dc, electrodes):
src1, rec1, rec2, src2 = electrodes.transpose((2, 0, 1))
# Calculate the euclidean distance between electrode pairs
r1 = euclidean.dist(src1, rec1)
r2 = euclidean.dist(src2, rec1)
r3 = euclidean.dist(src1, rec2)
r4 = euclidean.dist(src2, rec2)
if res_mesh == 1:
deltaV = dc * res_mesh / (2 * np.pi) * ((1 / r1 - 1 / r2) -
(1 / r3 - 1 / r4))
resistance = deltaV / dc
rho_a = resistance * geo_factor
return (rho_a)
def fwdEngine(res_mesh,geo_factor,dc,electrodes):
src1,rec1,rec2,src2 = electrodes.transpose((2,0,1))
# Calculate the euclidean distance between electrode pairs
r1 = euclidean.dist(src1,rec1)
r2 = euclidean.dist(src2,rec1)
r3 = euclidean.dist(src1,rec2)
r4 = euclidean.dist(src2,rec2)
if res_mesh == 1:
deltaV = dc*res_mesh/(2*np.pi)*((1/r1-1/r2)-(1/r3-1/r4))
resistance = deltaV/dc
rho_a = resistance*geo_factor
return (rho_a)
def new_predict(movie_id):
movie_name1 = movie_dict[str(movie_id)]["title"]
eucl = []
for k, v in USER_RATING.iteritems():
movie_name2 = movie_dict[k]["title"]
eucl.append((euclidean.dist(user_ratings, movie_name1, movie_name2), v, k))
sorted_list = sorted(eucl)
score = sorted_list[0][0]
sorted_list.reverse()
highest_ratio = sorted_list[0][1]
movie_id2 = sorted_list[0][2]
prediction = "%.1f" % round(score * highest_ratio)
print sorted_list
print "Best Euclidean guess for movie %s: %s is %s stars" % (movie_id, movie_name1, prediction)
def knn(movie_ratings, predict_movie, prev_rated,num_neighbors):
#prev_rated is a dictionary of previously rated movies (movie_id: rating)
eucl_dist = {}
for k,v in prev_rated:
print prev_rated
eucl_dist[str(dist(movie_ratings,predict_movie,v))] = (k, v)
# sort keys of eucl_dict
eucl_keys = [int(x) for x in eucl_dist.keys()]
sorted_keys = sorted(eucl_keys)
neighbors = sorted_keys[0:num_neighbors]
user_rating_total = 0
for neighbor in neighbors:
user_rating_total += eucl_dist[neighbor][1]
knn_est = user_rating_total/num_neighbors
return knn_est
"""
Given two d-dimensional vectors u and v, the ways to compute their distance are various.
"""
import numpy as np
N = int(input('input N-dimension : '))
u = [np.random.randint(10) for x in range(N)]
v = [np.random.randint(10) for x in range(N)]
print('u vector :', u)
print('v vector :', v)
import euclidean
import manhattan
import hamming
import cosine
import chebyshev
print('Euclidean distance :', euclidean.dist(u, v))
print('Manhattan distance :', manhattan.dist(u, v))
print('Hamming distance :', hamming.dist(u, v))
print('Cosine distance :', cosine.dist(u, v))
print('Chebyshev distance :', chebyshev.dist(u, v))