def adaline_test_i_b():
input_file = "bitmap_data.csv"
df = pd.read_csv(input_file, header=None)
df.head()
# X: values, y: targets
# extract features
X = df.iloc[:, 0:77].values
X = add_biases(X)
# extract the label column
y = df.iloc[:, 77].values
# Number 5:
y_5 = np.where(y == 5, 1, 0)
# Number 6:
y_6 = np.where(y == 6, 1, 0)
# Number 8:
y_8 = np.where(y == 8, 1, 0)
# Number 9:
y_9 = np.where(y == 9, 1, 0)
sets = [y_5, y_6, y_8, y_9]
predictions = []
y_test = []
i = 0
for set in sets:
# split data into train and test sets
X_train = np.concatenate((X[:8], X[11:19], X[22:30], X[33:41]))
X_test = np.concatenate((X[8:11], X[19:22], X[30:33], X[41:]))
y_train = np.concatenate((set[:8], set[11:19], set[22:30], set[33:41]))
y_test_tmp = np.concatenate(
(set[8:11], set[19:22], set[30:33], set[41:]))
if(i == 0): # only print the first time
print_bitmap(X_test)
y_test.append(y_test_tmp)
# print(X_train)
# print(y_train)
# live plot
plot = True
# 3d plot
d3 = False
adaline_implementation(y_train, y_test_tmp,
X_train, X_test, plot, d3)
def adaline_test_i_b():
# import and ready input file
input_file = "iris.csv"
df = pd.read_csv(input_file, header=None)
df.head()
# X: values, y: targets
# extract features
X = df.iloc[:, 0:4].values
# extract the label column
y = df.iloc[:, 4].values
# normalize the data attributes
X = preprocessing.normalize(X)
# TODO
# add biases
X = add_biases(X)
# Setosa:
y_setosa = np.where(y == 'Setosa', 1, 0)
# Versicolor:
y_versicolor = np.where(y == 'Versicolor', 1, 0)
# Virginica:
y_virginica = np.where(y == 'Virginica', 1, 0)
sets = [y_setosa, y_versicolor, y_virginica]
predictions = []
y_test = []
i = 0
for set in sets:
# split data into train and test sets
X_train, X_test, y_train, y_test_tmp = train_test_split(
X, set, test_size=0.2, random_state=123)
y_test.append(y_test_tmp)
# live plot
plot = True
# 3d plot
d3 = True
adaline_implementation(y_train, y_test_tmp, X_train, X_test, plot, d3)
def main():
plotting = int(input("0. Live Plot\n1. Only Results\n") or 0)
live_plotting = plotting == 0
X, y, is3d, isIris = load_file()
clusters_num = int(input("Δώσε αριθμό κέντρων: ") or 2)
max_iter = int(input('Δώσε αριθμό μέγιστων επαναλύψεων: ') or 300)
update_plot = int(input("Update Plot ανά πόσες εποχές: ") or max_iter / 10)
tol = float(input('Δώσε tolerance: ') or 0.001)
km = KMeans(k=clusters_num, max_iter=max_iter, tol=tol)
km.fit(X, y, live_plotting, update_plot, is3d, isIris)
dMax = max([np.abs(c1 - c2) for c1 in km.centroids for c2 in km.centroids])
std = dMax / np.sqrt(2 * clusters_num)
patterns = []
targets = []
for classification in km.classifications:
for featureset in km.classifications[classification]:
tmp = []
for centroid in km.centroids:
r = rbf(featureset, km.centroids[centroid], std)
tmp.append(r)
tmp = np.array(tmp)
patterns.append(tmp)
targets.append(classification)
patterns = np.array(patterns)
targets = np.array(targets)
patterns = add_biases(patterns)
patterns_train, patterns_test, targets_train, targets_test = train_test_split(
patterns, targets, test_size=0.2, random_state=123)
adaline_implementation(targets_train,
targets_test,
patterns_train,
patterns_test,
plot=live_plotting,
d3=False)
def adaline_test_i_b():
file = input("Δώσε input file(a,b,c,d): ")
input_file = 'data_package_%s.csv' % file
df = pd.read_csv(input_file, header=0)
df = df._get_numeric_data()
# targets
targets_file = 'data_package_values_%s.csv' % file
targets_df = pd.read_csv(targets_file, header=0)
targets_df = targets_df._get_numeric_data()
values = df.values
targets = targets_df.values
# plot_data(targets)
values = add_biases(values)
patterns_train, patterns_test, targets_train, targets_test = train_test_split(
values, targets, test_size=0.2, random_state=123)
# plot_testing_data(targets_train, targets_test)
plot = True
d3 = False
adaline_implementation(targets_train, targets_test, patterns_train,
patterns_test, plot, d3)