def num(self, x, y): if functions.dist(self, x) > functions.dist(self, y): return 1 elif functions.dist(self, x) == functions.dist(self, y): return 0 else: return -1
def get_in_SOF(self): self.planets_in_SOF = [] for p in self.game.all_planets: if fn.dist(self.pos,p.pos) <= self.radius: self.planets_in_SOF.append(p)
def gsort(self, x): nGrid = [] x.sort(self.num) for item in x: nGrid.append([item, functions.dist(self, item)]) del x return nGrid
async def getnearby(lat: float, lng: float, radius: float): res = session.query(schemas.Places).all() nearby = [] for entry in res: d = dist(lat, lng, entry.latitude, entry.longitude) if (d < radius): nearby.append(entry) return nearby
async def getnearbyf(lat: float, lng: float, radius: float): query = "select * from places where (point({0},{1}) <@> (point(longitude,latitude))) < {2}".format( lng, lat, radius) query = text(query) res = conn.execute(query) nearby = [] for entry in res: d = dist(lat, lng, entry.latitude, entry.longitude) if (d < radius): nearby.append(entry) return nearby
# We understand from it that by erasing full rows we did not change the balance dramatically but we did loose # information. Since We have less negative than positive patients, we don't want to loose patients from the negative # group. We will complete the missing values by random sampling of each series values. # Replace nan's with random samples of each series values: from functions import nan2samp T1D_clean = nan2samp(T1D_dataset) # section 2 lbl = np.ravel(T1D_clean['Diagnosis']) X_train, X_test, y_train, y_test = train_test_split(T1D_clean, lbl, test_size=0.2, random_state=0, stratify=lbl) # Section 3.a - show that the distribution of the features is similar between test and train # Using a function to create a table of positive rates for every feature in the train/test groups: from functions import dist_table as dist X_test_dummy = pd.get_dummies(X_test, dummy_na=False, drop_first=True) X_train_dummy = pd.get_dummies(X_train, dummy_na=False, drop_first=True) d_table = dist(X_train_dummy, X_test_dummy) print(d_table.transpose()) # Section 3.b - show the relationship between feature and label: from functions import feat_lab_cor as fl_cor fl_cor(T1D_dataset) # Section 3.c - additional plots #
from functions import converttime from functions import toprint from functions import date from functions import observations from functions import setdata from functions import getdata from functions import deletedata from functions import createtable from texts import menu_text from texts import invalidoption_text #Menu inicial menu_text() option = input() if option == '1': total = dist() totaltime = timerun() adjusted = adjust(totaltime) finalpace = calcpace(adjusted, total) convertedpace = converttime(finalpace) impress = toprint(convertedpace) elif option == '2': total = dist() totaltime = timerun() adjusted = adjust(totaltime) calctemp = predictrun(adjusted, total) convertedtemprun = converttime(calctemp) impress = toprint(convertedtemprun) elif option == '3': createtable() thedate = date()
C_x = np.random.randint(0, np.max(X) - 20, size=k) # Y coordinates of random centroids C_y = np.random.randint(0, np.max(X) - 20, size=k) C = np.array(list(zip(C_x, C_y)), dtype=np.float32) print(C) plt.scatter(f1, f2, c='#050505', s=7) plt.scatter(C_x, C_y, marker='*', s=200, c='g') plt.show() # To store the value of centroids when it updates C_old = np.zeros(C.shape) # Cluster labels (0, 1, 2) clusters = np.zeros(len(X)) #error function - distance between new centroids and old centroids error = func.dist(C, C_old, None) # Loop will run until the error becomes 0 while error != 0: # Assigning each value to its closest cluster for i in range(len(X)): distances = func.dist(X[i], C) cluster = np.argmin(distances) clusters[i] = cluster # Storing the old centroid values C_old = deepcopy(C) # Finding the new centroids by taking the average value for i in range(k): points = [X[j] for j in range(len(X)) if clusters[j] == i] C[i] = np.mean(points, axis=0) error = func.dist(C, C_old, None)
def get_travel_info(self,planet,travel_bonus): self.travel_time = fn.travel_time(fn.dist(self.instance[0].pos,planet.pos),self.instance[0].game.space_travel_unit)/travel_bonus self.travel_cost = fn.travel_formula(self.travel_time)