def model_bin_train(self, data_row, truth, num_bins=2):
#TODO add epsilon
model = {}
cutoffsc = [[] for _ in range(len(data_row[0]))]
dmat = np.matrix(data_row)
drange = dmat.max() - dmat.min()
bin_size = float(drange) / num_bins
data_col = hw3.transpose_array(data_row)
for j in range(len(data_col)):
#cutoffsc.append([min(data_col)[0] + bin_size * i for i in range(num_bins)])
mu = np.asarray(data_col[j]).mean()
low_mu = np.asarray([data_col[j][i] for i in range(len(data_col[j])) if data_col[j][i] < mu]).mean()
high_mu = np.asarray([data_col[j][i] for i in range(len(data_col[j])) if data_col[j][i] > mu]).mean()
if num_bins == 4:
cutoffsc[j] = [min(data_col)[0], low_mu, mu, high_mu]
else:
cutoffsc[j] = [min(data_col)[0], (low_mu - min(data_col)[0])/2, mu, (high_mu-mu)/2, high_mu, (max(data_col)[0]-high_mu)/2]
cutoffs = [dmat.min() + bin_size * i for i in range(num_bins)]
#epsilon = float(alpha * 1) / len(covar_matrix)
for label in [0,1]:
# transpose to go by column
sub_data = hw3.transpose_array(hw3.get_sub_at_value(data_row, truth, label))
model[label] = hw3.bins_per_column(sub_data, cutoffs)
model[label] = hw3.bins_per_column_by_col(sub_data, cutoffsc)
# probability of bin given label
self.y_prob = float(sum(truth))/len(truth)
self.cutoffs = cutoffsc
return model
def testTransposeArray():
dfup = hw3.load_and_normalize_spambase()
cols = dfup.columns[0:3]
sub = utils.train_subset(dfup, cols, 5)
up = hw3.pandas_to_data(sub)
print up
trans = hw3.transpose_array(up)
print trans
def get_prob_over(data_by_row, mus):
"""
Return array of arrays
column[i] = [probability_above]
"""
probability_above_mu = []
size = len(data_by_row)
by_col = hw3.transpose_array(data_by_row)
for col in range(len(by_col)):
total_over = 0
column = by_col[col]
mu_col = mus[col]
var_col = utils.variance(by_col[col], size)
for row in range(len(column)):
if column[row] > mu_col:
total_over += 1
probability_above_mu.append(float(total_over)/size)
return probability_above_mu
def model_average_predict(self, data_row, theta=.5):
""" For each row calculate the probability
that y is 1 and the probability that y is 0
P(Y|X) = ( P(X|Y) * P(Y) ) / ( P(X) )
P(X) = prob_over (probability that x is above average for column)
P(X|Y) = prob_over_given_c (probability that x is above average when y = c for column)
P(Y) = prob_y ( probability of y )
"""
mus = hw3.get_mus(data_row)
data_cols = hw3.transpose_array(data_row)
prob_over_given_1 = self.model[0]
prob_over_given_0 = self.model[1]
prob_over = self.model[2]
prob_y1 = self.model[3]
predict = []
for r in range(len(data_row)):
row = data_row[r]
prob_1 = 1
prob_0 = 1
for c in range(len(row)):
mu = mus[c]
if row[c] > mu:
prob_x1 = prob_over_given_1[c]
prob_x0 = prob_over_given_0[c]
prob_xover = prob_over[c]
else:
prob_x1 = 1 - prob_over_given_1[c]
prob_x0 = 1 - prob_over_given_0[c]
prob_xover = 1 - prob_over[c]
prob_1 = prob_1 * prob_x1 #* prob_y1 #/ prob_xover #P(X|Y) * P(Y)
prob_0 = prob_0 * prob_x0 #* (1-prob_y1) #/ prob_xover
#prob_1 = prob_1 + np.log(prob_x1) + np.log(prob_y1)
#prob_0 = prob_0 + np.log(prob_x0) + np.log(1-prob_y1)
prob_1 = prob_1 * prob_y1
prob_0 = prob_0 * (1 - prob_y1)
prob_norm = float(prob_1)/(prob_0 + prob_1)
if prob_norm > theta:
predict.append(1)
else:
predict.append(0)
return predict
def initialize(self, data, k=2):
# start with k = 2 and std_dev = 1
self.k = k
self.labels = [ki for ki in range(self.k)]
models = [EMModel() for _ in range(self.k)]
mucheat = mu_cheat(hw3.transpose_array(data), k)
for ki in range(self.k):
#models[ki].random_mus(data)
models[ki].mu = mucheat[ki]
self.labels = self.assign_labels(data, models)
#self.labels = self.assign_labels2(data, model)
self.prevent_empty(data)
for ki in range(self.k):
sub_data = hw3.get_sub_at_value(data, self.labels, ki)
#models[ki].sigma = hw3.get_covar(sub_data)
models[ki].sigma = hw3.get_covar(data)
#models[ki].weight = float(len(sub_data)) / len(data)
models[ki].weight = .5
models[ki].likelihood = self.expectation(data, models[ki]) # multivarate_normal
self.models = models
def test_covar_matrix():
arr = get_test_data()
print arr
y = [1, 0, 0, 0]
print hw3.get_covar(arr, y)
print np.cov(hw3.transpose_array(arr), y)
def set_mus(self, data):
mu = []
by_col = hw3.transpose_array(data) # to go by column
for j in range(len(by_col)):
mu.append(np.mean(by_col[j]))
self.mu = mu
