在目标c

Question

我正在尝试获取URL地址的元数据，而我正在使用此代码

NSMutableURLRequest *request = [NSMutableURLRequest requestWithURL:[NSURL URLWithString:@"https://www.youtube.com/watch?v=1cuMAeGOCng"]];
    [request setValue:@"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Safari/601.1.42"
   forHTTPHeaderField:@"User-Agent"];

    self.session = [NSURLSession sessionWithConfiguration:[NSURLSessionConfiguration defaultSessionConfiguration]];
    NSURLSessionTask *task = [self.session dataTaskWithRequest:request completionHandler:^(NSData * _Nullable data, NSURLResponse * _Nullable response, NSError * _Nullable error) {
        NSLog(@"%@",data);
    }];
    [task resume];

这是对的吗？ 返回的数据也被编码，无法解释如何解码！任何线索？

由于

Answer 1

#! /usr/bin/python3.6

# Code based on:
# https://datasciencelab.wordpress.com/2013/12/12/clustering-with-k-means-in-python/

import matplotlib.pyplot as plt
import numpy as np
import random

##### Simulation START #####
# Generate possible points.
def possible_points(n=20):
    y=list(np.linspace( -1, 1, n ))
    x=[-1.2]
    X=[]
    for i in list(range(1,n)):
        x.append(x[i-1]+random.uniform(-2/n,2/n) )
    for a,b in zip(x,y):
        X.append(np.array([a,b]))
    X = np.array(X)
    return X

# Generate sample
def init_board_gauss(N, k):
    n = float(N)/k
    X = []
    for i in range(k):
        c = (random.uniform(-1, 1), random.uniform(-1, 1))
        s = random.uniform(0.05,0.5)
        x = []
        while len(x) < n:
            a, b = np.array([np.random.normal(c[0], s), np.random.normal(c[1], s)])
            # Continue drawing points from the distribution in the range [-1,1]
            if abs(a) < 1 and abs(b) < 1:
                x.append([a,b])
        X.extend(x)
    X = np.array(X)[:N]
    return X
##### Simulation END #####    

# Identify points for each center.
def cluster_points(X, mu):
    clusters  = {}
    for x in X:
        bestmukey = min([(i[0], np.linalg.norm(x-mu[i[0]])) \
                    for i in enumerate(mu)], key=lambda t:t[1])[0]
        try:
            clusters[bestmukey].append(x)
        except KeyError:
            clusters[bestmukey] = [x]
    return clusters

# Get closest possible point for each cluster.
def closest_point(cluster,possiblePoints):
    closestPoints=[]
    # Check average distance for each point.
    for possible in possiblePoints:
        distances=[]
        for point in cluster:
            distances.append(np.linalg.norm(possible-point))
            closestPoints.append(np.sum(distances)) # minimize total distance
            # closestPoints.append(np.mean(distances))
    return possiblePoints[closestPoints.index(min(closestPoints))]

# Calculate new centers.
# Here the 'coast constraint' goes.
def reevaluate_centers(clusters,possiblePoints):
    newmu = []
    keys = sorted(clusters.keys())
    for k in keys:
        newmu.append(closest_point(clusters[k],possiblePoints))
    return newmu

# Check whether centers converged.
def has_converged(mu, oldmu):
    return (set([tuple(a) for a in mu]) == set([tuple(a) for a in oldmu]))

# Meta function that runs the steps of the process in sequence.
def find_centers(X, K, possiblePoints):
    # Initialize to K random centers
    oldmu = random.sample(list(possiblePoints), K)
    mu = random.sample(list(possiblePoints), K)
    while not has_converged(mu, oldmu):
        oldmu = mu
        # Assign all points in X to clusters
        clusters = cluster_points(X, mu)
        # Re-evaluate centers
        mu = reevaluate_centers(clusters,possiblePoints)
    return(mu, clusters)


K=3
X = init_board_gauss(30,K)
possiblePoints=possible_points()
results=find_centers(X,K,possiblePoints)

# Show results

# Show constraints and clusters
# List point types
pointtypes1=["gx","gD","g*"]

plt.plot(
    np.matrix(possiblePoints).transpose()[0],np.matrix(possiblePoints).transpose()[1],'m.'
    )

for i in list(range(0,len(results[0]))) :
    plt.plot(
        np.matrix(results[0][i]).transpose()[0], np.matrix(results[0][i]).transpose()[1],pointtypes1[i]
        )

pointtypes=["bx","yD","c*"]
# Show all cluster points
for i in list(range(0,len(results[1]))) :
    plt.plot(
        np.matrix(results[1][i]).transpose()[0],np.matrix(results[1][i]).transpose()[1],pointtypes[i]
        )
plt.show()