import time
import numpy as npimport matplotlib.pyplot as plt
from sklearn.cluster import MiniBatchKMeans, KMeansfrom sklearn.metrics.pairwise import pairwise_distances_argminfrom sklearn.datasets import make_blobs
# Generate sample datanp.random.seed(0)
batch_size = 45centers = [[1, 1], [-1, -1], [1, -1]]n_clusters = len(centers)X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)
# Compute clustering with Means
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)t0 = time.time()k_means.fit(X)t_batch = time.time() - t0
# Compute clustering with MiniBatchKMeans
mbk = MiniBatchKMeans(init='k-means++', n_clusters=3, batch_size=batch_size,                      n_init=10, max_no_improvement=10, verbose=0)t0 = time.time()mbk.fit(X)t_mini_batch = time.time() - t0
# Plot resultfig = plt.figure(figsize=(8, 3))fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)colors = ['#4EACC5', '#FF9C34', '#4E9A06']
# We want to have the same colors for the same cluster from the# MiniBatchKMeans and the KMeans algorithm. Let's pair the cluster centers per# closest one.k_means_cluster_centers = k_means.cluster_centers_order = pairwise_distances_argmin(k_means.cluster_centers_,                                  mbk.cluster_centers_)mbk_means_cluster_centers = mbk.cluster_centers_[order]
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers)mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers)
# KMeansfor k, col in zip(range(n_clusters), colors):    my_members = k_means_labels == k    cluster_center = k_means_cluster_centers[k]    plt.plot(X[my_members, 0], X[my_members, 1], 'w',            markerfacecolor=col, marker='.')    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,            markeredgecolor='k', markersize=6)plt.title('KMeans')plt.xticks(())plt.yticks(())
plt.show()

from scipy import specialimport matplotlib.pyplot as pltimport numpy as np
def drumhead_height(n, k, distance, angle, t):    kth_zero = special.jn_zeros(n, k)[-1]    return np.cos(t) * np.cos(n*angle) * special.jn(n, distance*kth_zero)
theta = np.r_[0:2*np.pi:50j]radius = np.r_[0:1:50j]x = np.array([r * np.cos(theta) for r in radius])y = np.array([r * np.sin(theta) for r in radius])z = np.array([drumhead_height(1, 1, r, theta, 0.5) for r in radius])

fig = plt.figure()ax = fig.add_axes(rect=(0, 0.05, 0.95, 0.95), projection='3d')ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap='RdBu_r', vmin=-0.5, vmax=0.5)ax.set_xlabel('X')ax.set_ylabel('Y')ax.set_xticks(np.arange(-1, 1.1, 0.5))ax.set_yticks(np.arange(-1, 1.1, 0.5))ax.set_zlabel('Z')plt.show()

import nltkfrom nltk.corpus import treebank
# 首次使用需要下载nltk.download('punkt')nltk.download('averaged_perceptron_tagger')nltk.download('maxent_ne_chunker')nltk.download('words')nltk.download('treebank')
sentence = """At eight o'clock on Thursday morning Arthur didn't feel very good."""# Tokenizetokens = nltk.word_tokenize(sentence)tagged = nltk.pos_tag(tokens)
# Identify named entitiesentities = nltk.chunk.ne_chunk(tagged)
# Display a parse treet = treebank.parsed_sents('wsj_0001.mrg')[0]t.draw()

  import spacy
  texts = [      "Net income was $9.4 million compared to the prior year of $2.7 million.",      "Revenue exceeded twelve billion dollars, with a loss of $1b.",  ]
  nlp = spacy.load("en_core_web_sm")  for doc in nlp.pipe(texts, disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"]):      # Do something with the doc here      print([(ent.text, ent.label_) for ent in doc.ents])

[('$9.4 million', 'MONEY'), ('the prior year', 'DATE'), ('$2.7 million', 'MONEY')][('twelve billion dollars', 'MONEY'), ('1b', 'MONEY')]

# Beat tracking exampleimport librosa
# 1. Get the file path to an included audio examplefilename = librosa.example('nutcracker')
# 2. Load the audio as a waveform `y`#    Store the sampling rate as `sr`y, sr = librosa.load(filename)
# 3. Run the default beat trackertempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)print('Estimated tempo: {:.2f} beats per minute'.format(tempo))
# 4. Convert the frame indices of beat events into timestampsbeat_times = librosa.frames_to_time(beat_frames, sr=sr)

import matplotlib.pyplot as pltimport pandas as pdimport numpy as np
ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))ts = ts.cumsum()
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))df = df.cumsum()df.plot()plt.show()

# plot_multi_curve.pyimport numpy as npimport matplotlib.pyplot as pltx = np.linspace(0.1, 2 * np.pi, 100)y_1 = xy_2 = np.square(x)y_3 = np.log(x)y_4 = np.sin(x)plt.plot(x,y_1)plt.plot(x,y_2)plt.plot(x,y_3)plt.plot(x,y_4)plt.show()

import seaborn as snsimport matplotlib.pyplot as pltsns.set_theme(style="ticks")
df = sns.load_dataset("penguins")sns.pairplot(df, hue="species")plt.show()

$ pip install orange3

$ orange-canvas

from pybrain.structure import FeedForwardNetworkn = FeedForwardNetwork()

from pybrain.structure import LinearLayer, SigmoidLayer
inLayer = LinearLayer(2)hiddenLayer = SigmoidLayer(3)outLayer = LinearLayer(1)

n.addInputModule(inLayer)n.addModule(hiddenLayer)n.addOutputModule(outLayer)

from pybrain.structure import FullConnectionin_to_hidden = FullConnection(inLayer, hiddenLayer)hidden_to_out = FullConnection(hiddenLayer, outLayer)

n.addConnection(in_to_hidden)n.addConnection(hidden_to_out)

n.sortModules()

import numpy as npimport milkfeatures = np.random.rand(100,10)labels = np.zeros(100)features[50:] += .5labels[50:] = 1learner = milk.defaultclassifier()model = learner.train(features, labels)
# Now you can use the model on new examples:example = np.random.rand(10)print(model.apply(example))example2 = np.random.rand(10)example2 += .5print(model.apply(example2))

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
# 数据加载(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# 数据预处理train_images, test_images = train_images / 255.0, test_images / 255.0
# 模型构建model = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.Flatten())model.add(layers.Dense(64, activation='relu'))model.add(layers.Dense(10))
# 模型编译与训练model.compile(optimizer='adam',              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),              metrics=['accuracy'])history = model.fit(train_images, train_labels, epochs=10,                     validation_data=(test_images, test_labels))

# 导入库import torchfrom torch import nnfrom torch.utils.data import DataLoaderfrom torchvision import datasetsfrom torchvision.transforms import ToTensor, Lambda, Composeimport matplotlib.pyplot as plt
# 模型构建device = "cuda" if torch.cuda.is_available() else "cpu"print("Using {} device".format(device))
# Define modelclass NeuralNetwork(nn.Module):    def __init__(self):        super(NeuralNetwork, self).__init__()        self.flatten = nn.Flatten()        self.linear_relu_stack = nn.Sequential(            nn.Linear(28*28, 512),            nn.ReLU(),            nn.Linear(512, 512),            nn.ReLU(),            nn.Linear(512, 10),            nn.ReLU()        )
    def forward(self, x):        x = self.flatten(x)        logits = self.linear_relu_stack(x)        return logits
model = NeuralNetwork().to(device)
# 损失函数和优化器loss_fn = nn.CrossEntropyLoss()optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
# 模型训练def train(dataloader, model, loss_fn, optimizer):    size = len(dataloader.dataset)    for batch, (X, y) in enumerate(dataloader):        X, y = X.to(device), y.to(device)
        # Compute prediction error        pred = model(X)        loss = loss_fn(pred, y)
        # Backpropagation        optimizer.zero_grad()        loss.backward()        optimizer.step()
        if batch % 100 == 0:            loss, current = loss.item(), batch * len(X)            print(f"loss: {loss:&>7f}  [{current:&>5d}/{size:&>5d}]")

import theanoimport theano.tensor as Tx = T.dvector('x')y = x ** 2J, updates = theano.scan(lambda i, y,x : T.grad(y[i], x), sequences=T.arange(y.shape[0]), non_sequences=[y,x])f = theano.function([x], J, updates=updates)f([4, 4])

from keras.models import Sequentialfrom keras.layers import Dense
# 模型构建model = Sequential()model.add(Dense(units=64, activation='relu', input_dim=100))model.add(Dense(units=10, activation='softmax'))
# 模型编译与训练model.compile(loss='categorical_crossentropy',              optimizer='sgd',              metrics=['accuracy'])model.fit(x_train, y_train, epochs=5, batch_size=32)         

import mxnet as mxfrom mxnet import gluonfrom mxnet.gluon import nnfrom mxnet import autograd as agimport mxnet.ndarray as F
# 数据加载mnist = mx.test_utils.get_mnist()batch_size = 100train_data = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)val_data = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
# CNN模型class Net(gluon.Block):    def __init__(self, **kwargs):        super(Net, self).__init__(**kwargs)        self.conv1 = nn.Conv2D(20, kernel_size=(5,5))        self.pool1 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))        self.conv2 = nn.Conv2D(50, kernel_size=(5,5))        self.pool2 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))        self.fc1 = nn.Dense(500)        self.fc2 = nn.Dense(10)
    def forward(self, x):        x = self.pool1(F.tanh(self.conv1(x)))        x = self.pool2(F.tanh(self.conv2(x)))        # 0 means copy over size from corresponding dimension.        # -1 means infer size from the rest of dimensions.        x = x.reshape((0, -1))        x = F.tanh(self.fc1(x))        x = F.tanh(self.fc2(x))        return xnet = Net()# 初始化与优化器定义# set the context on GPU is available otherwise CPUctx = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})
# 模型训练# Use Accuracy as the evaluation metric.metric = mx.metric.Accuracy()softmax_cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
for i in range(epoch):    # Reset the train data iterator.    train_data.reset()    for batch in train_data:        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)        outputs = []        # Inside training scope        with ag.record():            for x, y in zip(data, label):                z = net(x)                # Computes softmax cross entropy loss.                loss = softmax_cross_entropy_loss(z, y)                # Backpropogate the error for one iteration.                loss.backward()                outputs.append(z)        metric.update(label, outputs)        trainer.step(batch.data[0].shape[0])    # Gets the evaluation result.    name, acc = metric.get()    # Reset evaluation result to initial state.    metric.reset()    print('training acc at epoch %d: %s=%f'%(i, name, acc))

# 导入需要的包import paddleimport numpy as npfrom paddle.nn import Conv2D, MaxPool2D, Linear
## 组网import paddle.nn.functional as F
# 定义 LeNet 网络结构class LeNet(paddle.nn.Layer):    def __init__(self, num_classes=1):        super(LeNet, self).__init__()        # 创建卷积和池化层        # 创建第1个卷积层        self.conv1 = Conv2D(in_channels=1, out_channels=6, kernel_size=5)        self.max_pool1 = MaxPool2D(kernel_size=2, stride=2)        # 尺寸的逻辑：池化层未改变通道数；当前通道数为6        # 创建第2个卷积层        self.conv2 = Conv2D(in_channels=6, out_channels=16, kernel_size=5)        self.max_pool2 = MaxPool2D(kernel_size=2, stride=2)        # 创建第3个卷积层        self.conv3 = Conv2D(in_channels=16, out_channels=120, kernel_size=4)        # 尺寸的逻辑：输入层将数据拉平[B,C,H,W] -&> [B,C*H*W]        # 输入size是[28,28]，经过三次卷积和两次池化之后，C*H*W等于120        self.fc1 = Linear(in_features=120, out_features=64)        # 创建全连接层，第一个全连接层的输出神经元个数为64， 第二个全连接层输出神经元个数为分类标签的类别数        self.fc2 = Linear(in_features=64, out_features=num_classes)    # 网络的前向计算过程    def forward(self, x):        x = self.conv1(x)        # 每个卷积层使用Sigmoid激活函数，后面跟着一个2x2的池化        x = F.sigmoid(x)        x = self.max_pool1(x)        x = F.sigmoid(x)        x = self.conv2(x)        x = self.max_pool2(x)        x = self.conv3(x)        # 尺寸的逻辑：输入层将数据拉平[B,C,H,W] -&> [B,C*H*W]        x = paddle.reshape(x, [x.shape[0], -1])        x = self.fc1(x)        x = F.sigmoid(x)        x = self.fc2(x)        return x

NDLNetworkBuilder=[        run=ndlLR        ndlLR=[      # sample and label dimensions      SDim=$dimension$      LDim=1          features=Input(SDim, 1)      labels=Input(LDim, 1)          # parameters to learn      B0 = Parameter(4)       W0 = Parameter(4, SDim)                  B = Parameter(LDim)      W = Parameter(LDim, 4)          # operations      t0 = Times(W0, features)      z0 = Plus(t0, B0)      s0 = Sigmoid(z0)               t = Times(W, s0)      z = Plus(t, B)      s = Sigmoid(z)              LR = Logistic(labels, s)      EP = SquareError(labels, s)          # root nodes      FeatureNodes=(features)      LabelNodes=(labels)      CriteriaNodes=(LR)      EvalNodes=(EP)      OutputNodes=(s,t,z,s0,W0)    ]