def __init__(self, data, target, itercount=2120, theta=0.25):
self.dm, self.dn = shape(data)
self.tm, self.tn = shape(target)
assert self.dm == self.tm
self.weight = np.random.rand(self.dn+1, self.tn)
self.data = np.hstack((data, -np.ones((self.dm, 1))))
self.target = target
self.itercount = itercount
self.theta = theta
def total_part(self, data, feature):
m, n = shape(data)
featureunique = np.unique(data[:, feature])
resultuniques = np.unique(data[:, self.RESULT])
allsum = 0
for fu in featureunique:
partdata = data[data[:, feature] == fu]
pdm, _ = shape(partdata)
partsum = 0
for u in resultuniques:
rum, _ = shape(partdata[partdata[:, self.RESULT] == u])
partsum = partsum - self.entropy(rum / pdm)
allsum = allsum - (pdm / m * partsum)
return allsum
def infogain(self, data):
m, n = shape(data)
s = self.total_entropy(data)
ds = [s - self.total_part(data, i) for i in range(n - 1)]
maxfeature = np.array(ds).argmax()
featurevalues = np.unique(data[:, maxfeature])
return maxfeature, featurevalues
def __init__(self, inputdata):
self.root = TreeNode()
self.inputdata = inputdata
self.features = inputdata[0]
self.data = inputdata[1:]
self.m, self.n = shape(inputdata)
self.RESULT = self.n - 1
def convert_target(target):
m, n = shape(target)
t = np.zeros((n, 3))
t[np.where(target==0), 0] = 1
t[np.where(target==1), 1] = 1
t[np.where(target==2), 2] = 1
return t
def total_entropy(self, data):
m, n = shape(data)
uniques = np.unique(data[:, self.RESULT])
e = 0
for u in uniques:
count = len(data[data[:, self.RESULT] == u])
e += self.entropy(count / m)
return e
def __init__(self, data, k=3, itercount=2, eta=0.25):
self.m, self.n = shape(data)
self.data = data
self.k = k
self.weight = np.random.rand(self.n, k)
self.itercount = itercount
self.normalised_data = self.normalise(self.data)
self.eta = eta
def __init__(self, data,
target,
hidden_node=5,
itercount=1000,
theta=0.5,
beta=1,
momentum=0.2,
mode='logistic'):
self.dm, self.dn = shape(data)
self.tm, self.tn = shape(target)
self.data = np.hstack((data, -np.ones((self.dm, 1))))
self.target = target
self.itercount = itercount
self.theta = theta
self.beta = beta
self.momentum = momentum
self.mode = mode
self.weight1 = (np.random.rand(self.dn+1, hidden_node)-0.5)*2/np.sqrt(self.dn)
self.weight2 = (np.random.rand(hidden_node+1, self.tn)-0.5)*2/np.sqrt(hidden_node)
def fwd(self, data):
hidden_output = np.dot(data, self.weight1)
tmp_output = sigmod(hidden_output, self.beta)
self.hidden_output = np.hstack((tmp_output, -np.ones((shape(tmp_output)[0], 1))))
output = np.dot(self.hidden_output, self.weight2)
if self.mode == 'linear':
return output
elif self.mode == 'logistic':
return sigmod(output)
elif self.mode == 'softmax':
return softmax(output)
else:
return None
def fwd(self, data=None):
if data is not None:
self.dm, self.dn = shape(data)
hidden = np.zeros((self.dm, self.count+1))
for i in range(self.count):
hidden[:, i] = np.exp(-np.sum(data - np.ones((1, self.dn)) * self.weight[:, i]**2, axis=1) / (2*self.sigma**2))
if self.normalise:
pass
output = self.pcn.fwd(hidden[:, :-1])
return output
def train(self):
change = range(self.dm)
updatew1 = np.zeros((shape(self.weight1)))
updatew2 = np.zeros((shape(self.weight2)))
for n in range(self.itercount):
fwd_output = self.fwd(self.data)
error = 0.5 * sum((self.target - fwd_output) ** 2)
if n % 200 == 0:
print '++++++++++++++++++'
print "Iteration: ", n, "\tError: ", error
if self.mode == 'linear':
delta_o = (self.target - fwd_output) / self.dm
elif self.mode == 'logistic':
delta_o = (self.target - fwd_output) * fwd_output * (1 - fwd_output)
elif self.mode == 'softmax':
delta_o = (self.target - fwd_output) / self.dm
else:
delta_o = 0
delta_h = self.hidden_output * \
(1.0 - self.hidden_output) *\
(np.dot(delta_o, np.transpose(self.weight2)))
updaetw1 = self.theta * (np.dot(np.transpose(self.data), delta_h[:,:-1])) + self.momentum * updatew1
updatew2 = self.theta * (np.dot(np.transpose(self.hidden_output), delta_o)) + self.momentum * updatew2
self.weight1 = self.weight1 + updatew1
self.weight2 = self.weight2 + updatew2
np.random.shuffle(change)
self.data = self.data[change, :]
self.target = self.target[change, :]
print 'WEIGHTS =================='
print self.weight1, self.weight2
def assigngroup(cls, data, k=0, centroid=None):
m, n = shape(data)
category = np.zeros((m, 1))
scoretable = np.zeros((m, k))
if centroid:
cls.centroid = centroid
if k:
cls.k = k
cdata = np.hstack((data, category))
for c in range(cls.k):
scoretable[:, c] = distance(cdata[:, :-1], cls.centroid[c, :])
cdata[:, -1] = scoretable.argmin(axis=1)
return cdata
def __init__(self, data, target, count=5, sigma=0, normalise=False):
self.dm, self.dn = shape(data)
self.data = data
self.target = target
self.count = count
self.normalise = normalise
self.hidden = np.zeros((self.dm, self.count+1))
if sigma == 0:
d = (data.max(axis=0) - data.min(axis=0)).max()
self.sigma = d / np.sqrt(2*self.count)
else:
self.sigma = sigma
self.pcn = Perceptron(self.hidden[:, :-1], target, itercount=3000)
self.weight = np.zeros((self.dn, self.count))
def train(cls, data, k=3, itercount=100):
m, n = shape(data)
#medoids = data[np.random.choice(m, k)] for numpy1.7
medoids = data[np.random.randint(0, m, k)]
old_medoids = None
min_score = maxint
scoretable = np.zeros((m, k+1))
category = np.zeros((m, 1))
cdata = np.hstack((data, category))
manhatton = partial(distance, mode='manhatton')
count = 0
while not np.array_equal(old_medoids, medoids) and\
count<itercount:
count += 1
for c in range(k):
scoretable[:, c] = manhatton(data, medoids[c])
mi = scoretable[:, :-1].argmin(axis=1)
cdata[:, -1] = mi
for i in range(m):
scoretable[i, -1] = scoretable[i, mi[i]]
score = scoretable[:, -1].sum()
if min_score > score:
min_score = score
medoids = data[np.random.randint(0, m, k)]
cls.assigneddata = cdata
'''
for c in range(k):
print '--'
print np.where(scoretable[:,-1]==c)
print scoretable[np.where(scoretable[:,-1]==c)]
medoids = data[np.random.randint(0, m, k)]
'''
print cdata
print min_score
def __init__(self, x, y, data, itercount=2000, size=0.5,
eta_bfinal=0.03,eta_nfinal=0.01,sizefinal=0.05):
self.m, self.n = shape(data)
self.x, self.y = x, y
self.xy = x * y
self.itercount = itercount
self.size = size
self.eta_bfinal = eta_bfinal
self.eta_nfinal = eta_nfinal
self.sizefinal = sizefinal
self.gridmap = np.mgrid[0:1:np.complex(0, x), 0:1:np.complex(0, y)]
self.weight = (np.random.rand(self.n, x*Y) - 0.5) * 2
self.grid = np.zeros(self.xy, self.xy)
for i in range(self.xy):
for j in range(self.xy):
self.grid[i, j] = np.sqrt((self.gridmap[0, i] - self.gridmap[0, j])**2 + (self.gridmap[1, i] - self.gridmap[1, j])**2)
self.grid[j, i] = self.grid[i, j]
def train(self, data=None, target=None):
if data is not None:
self.dm, self.dn = shape(data)
self.data = np.hstack((data, -np.ones((self.dm, 1))))
if target is not None:
self.target = target
change = range(self.dm)
for n in range(self.itercount):
error = 0.5 * sum((self.target - self.fwd()) ** 2)
if n % 200 == 0:
print '++++++++++++++++++'
print "Iteration: ", n, "\tError: ", error
self.weight += self.theta * np.dot(np.transpose(self.data), self.target - self.fwd())
np.random.shuffle(change)
self.data = self.data[change, :]
self.target = self.target[change, :]
print 'WEIGHT ==========================='
print self.weight
def train(cls, data, k=3, itercount=60):
m, n = shape(data)
category = np.zeros((m, 1))
cls.centroid = np.array([[5, 3, 5, 1], [4, 3, 1, 1], [5, 3, 1, 0]])
#cls.centroid = get_randomseed(data, (k, n))
cls.k = k
'''
u = m / k
d = [(u*i, u*(i+1), i) for i in range(k)]
for s, e, i in d:
category[s:e] = i
'''
cdata = np.hstack((data, category))
scoretable = np.zeros((m, k))
old_centroid = None
count = 0
while not np.array_equal(old_centroid, cls.centroid) and \
count < itercount:
old_centroid = cls.centroid.copy()
count += 1
for c in range(cls.k):
scoretable[:, c] = distance(data, cls.centroid[c, :])
#check the number of group
group = scoretable.argmin(axis=1)
if len(np.unique(group)) != cls.k:
cls.centroid = get_randomseed(data, (k, n))
continue
cdata[:, -1] = group
for c in range(k):
cls.centroid[c, :] = \
cdata[np.where(cdata[:, -1]==c)].mean(axis=0)[:-1]
cls.assigneddata = cdata
def score(ar):
m, n = shape(ar)
scoretable = np.zeros((m, 3))
groupids = set(ar[:, -1])
#groupids = np.unique(ar[:, -1])
A, B, S = range(3)
for i in range(m):
item = ar[i, :]
# get a(i)
subgroup = ar[np.where(ar[:, -1]==item[-1])]
scoretable[i, A] = np.mean(distance(subgroup[:, :-1], item[:-1]))
# get b(i)
scoretable[i, B] = np.min([np.min(distance(ar[np.where(ar[:, -1]==gid)][:, :-1], item[:-1])) for gid in groupids - {item[-1]}])
# get s(i)
scoretable[i, S] = (scoretable[i, B] - scoretable[i, A]) /\
max(scoretable[i, B], scoretable[i, A])
#print(scoretable[i, :])
return scoretable[:, S].mean()
def __init__(self, data, eps, minpts):
self.m, self.n = shape(data)
self.data = data
self.markeddata = np.hstack((data, np.zeros((self.m, 2))))
self.eps = eps
self.minpts = minpts
def fwd(self, data=None, func=threshold):
if data is not None:
self.dm, self.dn = shape(data)
self.data = np.hstack((data, -np.ones((self.dm, 1))))
return self._fwd(self.data)
def predict(self, test_input):
data = np.hstack((test_input, -np.ones((shape(test_input)[0], 1))))
return threshold(self.fwd(data))
def predict(self, test_data, func=threshold):
#data = np.hstack((test_data, -np.ones(1)))
data = np.hstack((test_data, -np.ones((shape(test_data)[0], 1))))
return self._fwd(data)
def score(self, input_data, target):
data = np.hstack((input_data, -np.ones((shape(input_data)[0], 1))))
output = self._fwd(data)
m = data.shape[0]
s = np.sum([(output[i]==target[i]).all() for i in range(m)])
return float(s) / float(m) * 100.0
def train(cls, data, model, m=5):
_, n = shape(data)
np.random.randint(0, n, (m, n))
pass
def initialise(self, data, target):
self.m, self.n = shape(data)
self.data = data
self.group = np.unique(target)
self.target = target
self.gaussians = None
def score(cls, target):
m, n = shape(cls.assigneddata)
return float(np.sum(cls.assigneddata[:, -1]==target)) / float(m) * 100
