def RV2OE(position,velocity,Mu):
pos = np.array(position)
vel = np.array(velocity)
v = np.linalg.norm(vel)
r = np.linalg.norm(pos)
dotV = mit.dotproduct(pos,vel)
RadialV = dotV/r
pos2 = np.reshape(pos, (1,3))
vel2 = np.reshape(vel, (1,3))
h = np.cross(pos2,vel2)
H = np.linalg.norm(h)
i = np.rad2deg(np.arccos(h[0,2]/H))
K = np.array([0, 0, 1])
n = np.cross(K,h)
n3 = np.reshape(n, (3,1))
N = np.linalg.norm(n)
if n[0,2] >= 0:
Omega = np.rad2deg(np.arccos(n[0,0]/N))
elif n[0,2] < 0:
Omega = 360.0 - np.rad2deg(np.arccos(n[0,0]/N))
e = (1/Mu)*((((v**2)-(Mu/r))*pos)-((r*RadialV)*vel))
ec = np.reshape(e, (1,3))
E = np.linalg.norm(ec)
dotA = mit.dotproduct(n3,e)
NE = N*E
if ec[0,2] >= 0:
w = np.rad2deg(np.arccos(dotA/NE))
elif ec[0,2] < 0:
w = 360 - np.rad2deg(np.arccos(dotA/NE))
dotT = mit.dotproduct(e,pos)
Er = E*r
if RadialV >= 0:
Theta = np.rad2deg(np.arccos(dotT/Er))
elif RadialV < 0:
Theta = 360 - np.rad2deg(np.arccos(dotT/Er))
Rp = ((H**2)/Mu)*(1/(1+E))
Ra = ((H**2)/Mu)*(1/(1-E))
a = 0.5 * (Rp + Ra)
return(E,a,i,Omega,w,Theta)
def cosine_sim(q_pp,tf_idf_wrt_doc,tf_idf_q,k,tw,dict_titles): ##tw = title weightage
result={} # stores dot product value of query vector with various doc vectors
for i in tf_idf_wrt_doc:
dot = mit.dotproduct(tf_idf_q,tf_idf_wrt_doc[i])
result[i] = dot
if(tw=="n" or tw=="N"):
result_sort = sorted(result.items(), key=operator.itemgetter(1),reverse=True)
else:
for w in word_tokenize(q_pp):
for j in dict_titles:
if w in word_tokenize(preprocess(dict_titles[j])):
result[j] = 1.2*result[j] ## increasing weightage of doc by 20% if query term is present in title..
result_sort = sorted(result.items(), key=operator.itemgetter(1),reverse=True)
print("\n\t\t\t\tTOP-{} DOCUMENTS RETREIVED BASED ON COSINE SIMILARITY ARE::\n".format(k))
print("="*124)
for r in range(k):
print(r+1,".",result_sort[r][0])
def test_happy_path(self):
"""simple dotproduct example"""
self.assertEqual(400, mi.dotproduct([10, 10], [20, 20]))
def test_happy_path(self):
"""simple dotproduct example"""
self.assertEqual(400, mi.dotproduct([10, 10], [20, 20]))
for line in data:
line = [line[0] - mean[0], line[1] - mean[1]]
# Training that network technically
#randomish weights
weights = [1, 1]
deltaW = [0, 0]
# learning rate
c = 0.1
# apply math to data, single iteration
for line in data:
# get dot product
y = mit.dotproduct(line, weights)
# make K
K = y * y
# get deltaW
deltaW[0] = c * ((line[0] * y) - (K * weights[0]))
deltaW[1] = c * ((line[1] * y) - (K * weights[1]))
# update weights
weights[0] = weights[0] + deltaW[0]
weights[1] = weights[1] + deltaW[1]
together = []
# dot product with the input data and new weights
