jetnew

import json
import openai
openai.api_key = "sk-"


def classifier(description, labels, label_descriptions):
    def classify(text):
        function = {
            "name": "Classify",
            "description": description,

jetnew

import os
from flask import Flask, session, abort, redirect, request
from google_auth_oauthlib.flow import Flow


app = Flask(__name__)
app.secret_key = os.urandom(24)

# Get client secrets file from https://console.cloud.google.com/apis/credentials -> OAuth client ID -> Web application
flow = Flow.from_client_secrets_file(

jetnew

import json
import openai
from inspect import signature, Parameter

openai.api_key = "sk-"


def func2tool(func):
    """Convert a function into OpenAI function calling API format."""
    assert func.__doc__, f"Function {func.__name__} must have a docstring."

jetnew

import numpy as np
from scipy.ndimage import convolve

dim = 10
board = np.zeros((dim, dim), dtype=np.uint8)
kernel = np.array([[1,1,1],
                   [1,0,1],
                   [1,1,1]], dtype=np.uint8)

def step(board, kernel=kernel):

jetnew

absl-py==0.12.0
alabaster==0.7.12
albumentations==0.1.12
altair==4.1.0
appdirs==1.4.4
argon2-cffi==20.1.0
arviz==0.11.2
astor==0.8.1
astropy==4.2.1
astunparse==1.6.3

jetnew

from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.metrics import mean_squared_error

timesteps = window_size-1
n_features = 1

model = Sequential()
model.add(LSTM(16, activation='relu', input_shape=(timesteps, n_features), return_sequences=True))
model.add(LSTM(16, activation='relu'))

jetnew

from skimage.util import view_as_windows

window_size = 5
timeseries = np.array([1,2,3,4,5,6,7,8,9,10])

trajectory_matrix = view_as_windows(timeseries, window_shape=window_size)

jetnew

from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed
from keras.models import Sequential

class LSTM_Autoencoder:
  def __init__(self, optimizer='adam', loss='mse'):
    self.optimizer = optimizer
    self.loss = loss
    self.n_features = 1
    
  def build_model(self):

jetnew

from sklearn.cluster import AgglomerativeClustering

clusters = 3
y_pred = AgglomerativeClustering(n_clusters=clusters).fit_predict(X)


from scipy.cluster.hierarchy import linkage, fcluster, dendrogram

clusters=5
cls = linkage(X, method='ward')

jetnew

from kenchi.outlier_detection.statistical import HBOS

hbos = HBOS(novelty=True).fit(X)
y_pred = hbos.predict(X)