收藏！27 个 Python 人工智能类库

import numpy as npimport mathimport randomimport time
start = time.time()for i in range(10):    list_1 = list(range(1,10000))    for j in range(len(list_1)):        list_1[j] = math.sin(list_1[j])print("使用纯Python用时{}s".format(time.time()-start))
start = time.time()for i in range(10):    list_1 = np.array(np.arange(1,10000))    list_1 = np.sin(list_1)print("使用Numpy用时{}s".format(time.time()-start))

from skimage import data, color, iofrom skimage.transform import rescale, resize, downscale_local_mean
image = color.rgb2gray(io.imread('h89817032p0.png'))
image_rescaled = rescale(image, 0.25, anti_aliasing=False)image_resized = resize(image, (image.shape[0] // 4, image.shape[1] // 4),                       anti_aliasing=True)image_downscaled = downscale_local_mean(image, (4, 3))plt.figure(figsize=(20,20))plt.subplot(221),plt.imshow(image, cmap='gray'),plt.title('Original')plt.xticks([]), plt.yticks([])plt.subplot(222),plt.imshow(image_rescaled, cmap='gray'),plt.title('Rescaled')plt.xticks([]), plt.yticks([])plt.subplot(223),plt.imshow(image_resized, cmap='gray'),plt.title('Resized')plt.xticks([]), plt.yticks([])plt.subplot(224),plt.imshow(image_downscaled, cmap='gray'),plt.title('Downscaled')plt.xticks([]), plt.yticks([])plt.show()

from PIL import Image, ImageDraw, ImageFont, ImageFilter
import random
# 随机字母:def rndChar():    return chr(random.randint(65, 90))
# 随机颜色1:def rndColor():    return (random.randint(64, 255), random.randint(64, 255), random.randint(64, 255))
# 随机颜色2:def rndColor2():    return (random.randint(32, 127), random.randint(32, 127), random.randint(32, 127))
# 240 x 60:width = 60 * 6height = 60 * 6image = Image.new('RGB', (width, height), (255, 255, 255))# 创建Font对象:font = ImageFont.truetype('/usr/share/fonts/wps-office/simhei.ttf', 60)# 创建Draw对象:draw = ImageDraw.Draw(image)# 填充每个像素:for x in range(width):    for y in range(height):        draw.point((x, y), fill=rndColor())# 输出文字:for t in range(6):    draw.text((60 * t + 10, 150), rndChar(), font=font, fill=rndColor2())# 模糊:image = image.filter(ImageFilter.BLUR)image.save('code.jpg', 'jpeg')

from SimpleCV import Image, Color, Display# load an image from imgurimg = Image('http://i.imgur.com/lfAeZ4n.png')# use a keypoint detector to find areas of interestfeats = img.findKeypoints()# draw the list of keypointsfeats.draw(color=Color.RED)# show the  resulting image. img.show()# apply the stuff we found to the image.output = img.applyLayers()# save the results.output.save('juniperfeats.png')

SyntaxError: Missing parentheses in call to 'print'. Did you mean print('unit test')?

import numpy as npimport mahotasimport mahotas.demos
from mahotas.thresholding import soft_thresholdfrom matplotlib import pyplot as pltfrom os import pathf = mahotas.demos.load('lena', as_grey=True)f = f[128:,128:]plt.gray()# Show the data:print("Fraction of zeros in original image: {0}".format(np.mean(f==0)))plt.imshow(f)plt.show()

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])
nlp.pipe 生成 Doc 对象，因此我们可以对它们进行迭代并访问命名实体预测：[('$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()

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 theano
                            

                            






                            

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