def main(station, k_neighbors, stations, transfers, beverage_data):
knc = KNeighborsClassifier(stations, transfers, beverage_data)
knc.fit(station, k_neighbors)
pred = knc.predict()
if pred['tea'] > pred['coffee']:
print(f'На станции {station} пьют чай')
elif pred['coffee'] > pred['tea']:
print(f'На станции {station} пьют кофе')
else:
print(f'На станции {station} пьют и чай и кофе')
return 0
import sklearn.utils as utils
import sklearn.datasets as datasets
from knn import KNeighborsClassifier
iris = datasets.load_iris()
X = iris.data
y = iris.target
ynames = iris.target_names
X, y = utils.shuffle(X, y, random_state=1)
train_set_size = 100
X_train = X[:train_set_size] # selects first 100 rows (examples) for train set
y_train = y[:train_set_size]
X_test = X[
train_set_size:] # selects from row 100 until the last one for test set
y_test = y[train_set_size:]
k = 5
knn = KNeighborsClassifier(k=k)
knn.fit(X_train, y_train)
y_pred_test = knn.predict(X_test)
print("Accuracy of KNN test set:", knn.score(y_pred_test, y_test))
import sklearn.utils as utils
import sklearn.datasets as datasets
from knn import KNeighborsClassifier
iris = datasets.load_iris()
X = iris.data
y = iris.target
ynames = iris.target_names
X, y = utils.shuffle(X, y, random_state=1)
train_set_size = 100
X_train = X[:
train_set_size] # selects first 100 rows (examples) for train set 100,4
y_train = y[:train_set_size] # 100
X_test = X[
train_set_size:] # selects from row 100 until the last one for test set
y_test = y[train_set_size:]
k = 5
knn = KNeighborsClassifier(k=k)
# knn.fit(X_train, y_train)
y_pred_test = knn.predict(X_train, X_test, y_train)
print("Accuracy of KNN test set:", knn.score(y_pred_test, y_test))
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import fetcher
import processor
import normalizer
from knn import KNeighborsClassifier
print('Fetching data...')
raw_data = fetcher.fetchVehicles()
print('Processing data...')
processed_data = processor.process(raw_data)
normalized_data_x = normalizer.normalize(processed_data["x"])
train_x, test_x, train_y, test_y = train_test_split(normalized_data_x,
processed_data["y"],
test_size=0.5)
classifier = KNeighborsClassifier(1)
classifier.fit(train_x, train_y)
predictions = classifier.predict(test_x)
print(f'Done! Accuracy: {accuracy_score(test_y, predictions)}')
# Build the mesh
for step_x in np.arange(min_x, max_x, resolution):
for step_y in np.arange(min_y, max_y, resolution):
mesh_in.append([step_x, step_y])
# Convert to numpy for performance reasons
mesh_in = np.array(mesh_in)
# Single pass to count elements used for debug
l = str(len(mesh_in))
print(
'Predicting.. | total points to predict = %s | This might take some time..'
% l)
mesh_out = np.array(knn.predict(mesh_in))
print('Computing mesh colors..')
c = lambda x: 'turquoise' if x == 1 else 'gold' if x == 2 else 'dodgerblue'
# Prepare the mesh colors
mesh_colors = [colors[c(p)] for p in mesh_out]
features_colors = [colors[c(p)] for p in labels]
print("Drawing graph | total points = " + l)
# Draw the decision boundaries
fig = plt.figure()
plt.title('Decision boundary for ' + df)
plt.ylabel('Feature 1')
outputData.append(data[20])
return inputData, outputData
if __name__ == '__main__':
inputToLearn, outputToLearn = readData("file.txt")
inputTest = []
outputTest = []
for i in range(10):
index = randint(0, len(inputToLearn) - 1)
inputTest.append(inputToLearn[index])
outputTest.append(outputToLearn[index])
inputToLearn.remove(inputToLearn[index])
outputToLearn.remove(outputToLearn[index])
classifier = KNeighborsClassifier()
classifier.fit(inputToLearn, outputToLearn)
accuracy = 0
for i in range(len(inputTest)):
if classifier.predict(inputTest[i]) == outputTest[i]:
accuracy += 1
print("The accuracy is: ", float(accuracy) / len(inputTest))
labels = []
for i in range(0, len(journeyPatternIds.keys())):
index = journeyPatternIds.keys()[i]
labels.append(journeyPatternIds[index])
test = []
for i in range(0, len(testSet.keys())):
index = testSet.keys()[i]
test.append([])
for t, lon, lat in testSet[index]:
test[i].append([lat, lon])
# Create the csv with the results of the KNN on the requested file
neigh = KNeighborsClassifier(n_neighbors=5)
neigh.fit(samples, labels)
result = neigh.predict(test, haversine)
dic = {"Test_Trip_ID": range(len(test)), "Predicted_JourneyPatternID": result}
out_df = pd.DataFrame(dic,
columns=['Test_Trip_ID', 'Predicted_JourneyPatternID'])
out_df.to_csv("testSet_JourneyPatternIDs.csv", sep='\t', index=False)
# Do a 10 fold for our KNN and save results in file
kf = KFold(n_splits=10)
f = open('10fold_2p_out.txt', 'w')
sum_acc = 0
count = 1
for train_index, test_index in kf.split(samples):
print("iteration ", count)