def index(building): buildings = building_list() flag = session.get('flag') url = session.get('url') session['flag'] = False session['url'] = '' print(building) if '*' in building: Html_file = open( os.path.join(mydir, 'templates/building/' + building + '.html'), "r") image = Html_file.read() x_coord, y_coord, screen_xs, screen_ys, zoomLevels, length = fetch_data( db, building.lower(), placeholder=None) building_string = 'building/template.html' return render_template(building_string, x_coord=x_coord, y_coord=y_coord, screen_xs=screen_xs, screen_ys=screen_ys, zoomLevels=zoomLevels, length=length, building=building, image=image, buildings=buildings, flag=flag, url=url) else: x_coord, y_coord, screen_xs, screen_ys, zoomLevels, length = fetch_data( db, building.lower(), placeholder=None) building_string = 'building/' + building.lower() + '.html' return render_template(building_string, x_coord=x_coord, y_coord=y_coord, screen_xs=screen_xs, screen_ys=screen_ys, zoomLevels=zoomLevels, length=length, buildings=buildings, flag=flag, url=url)
from sklearn.datasets import fetch_20newsgroups import helper as hlp import taskd as td from sklearn.linear_model import LogisticRegression from sklearn import metrics import sklearn.metrics as smet from sklearn.metrics import roc_curve import matplotlib.pyplot as plt categories = hlp.fetch_categories() twenty_train, twenty_test = hlp.fetch_data() hlp.classify_into_two_class(twenty_train) hlp.classify_into_two_class(twenty_test) svdListTrain = td.getsvdListTrain() nmfListTrain = td.getnmfListTrain() svdListTest = td.getsvdListTest() nmfListTest = td.getnmfListTest() classifier = LogisticRegression(C=10000) def classifyLR(train, test): classifier.fit(train, twenty_train.target) predicted = classifier.predict(test) predicted_probs = classifier.predict_proba(test) hlp.getStats(twenty_test.target, predicted) hlp.plot_roc(twenty_test.target, predicted_probs[:, 1], 'Logistic Regression') for min_df in [2, 5]:
import helper as hlp import task1 as t1 dataset = hlp.fetch_data() hlp.classify_into_two_class(dataset) labels = hlp.fetch_labels(dataset) tfidf_matrix = t1.getTFIDF_matrix(dataset, 3) km = hlp.getKmeans(2) km.fit(tfidf_matrix) hlp.getStats(labels, km.labels_)
def main(message): """ Background: This app fetches bike share data from the City of Toronto and the user's location from the client. It then determines the top 5 closest bike stations to the user and returns a route to the closest station prior to sending this information back to the client to be plotted on a map. Input: message: GEO data that comes from the client containing the user's longitude and latitude. Output: client_data: Socket_io sends (emits) data back to the client containing data from the top 5 closest stations to the user and any route information. This data will be used to plot the top 5 stations on a leaflet map. """ # Fetch 2 toronto bike share data feeds and store in stn_attr, stn_status stn_attr, stn_status = fetch_data() # Merge Station Information and Station Status all_stn_data = join_stn_data(stn_status, stn_attr) # Store User's Lat / Lon data from the client via socketio into a data variable # print(message) data = np.array(list(message.values())) # The user's lat / lon coordinates mylat = data[0][0] mylon = data[0][1] # mycoord will be used in some functions from helper.py mycoord = [mylat,mylon] # get a list of stations and their distances sorted by closest station to the user's lat / lon distances_to_all = get_closest_stn(mycoord, all_stn_data) # Get the closest stations lat / lon and the user's distance to the closest station (in km) closest_lat = distances_to_all[0][4] closest_lon = distances_to_all[0][5] closest_distance = distances_to_all[0][1] # If the user's distance is less than 2 km away from a station, # fetch all of the lat/lon positions between the user and the closest station. if closest_distance <= 2: # get route lat / lon data route = plot_route(mylat,mylon,closest_lat,closest_lon) if route == None: route = 0 print('No route data available') else: route print(route) else: # do nothing route = 0 print("Route too far to plot") # slice distances_to_all and get the top 5 closest stations top5closest_stns = distances_to_all[0:5] # Prepare to send the top 5 closest stations and route information back to the client client_data = [top5closest_stns, route] #print(client_data) # Send the top 5 closest stations and route information back to the client emit('my_response', {'data': client_data})