buttercutter

# [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torch.nn import functional as F
from einops import rearrange, repeat
from tqdm import tqdm

marcosan93

def visFBP(df, forecast):
    """
    Given two dataframes: before training df and a forecast df, returns
    a visual chart of the predicted values and actual values.
    """
    # Visual DF
    vis_df = df[['ds','Open']].append(forecast).rename(
        columns={'yhat': 'Prediction',
                 'yhat_upper': "Predicted High",
                 'yhat_lower': "Predicted Low"}

marcosan93

def fbpTrainPredict(df, forecast_period):
    """
    Uses FB Prophet and fits to a appropriately formatted DF. Makes a prediction N days into 
    the future based on given forecast period. Returns predicted values as a DF.
    """
    # Setting up prophet
    m = Prophet(
        daily_seasonality=True, 
        yearly_seasonality=True, 
        weekly_seasonality=True

artiya4u

#!/usr/bin/env bash
sudo apt install build-essential wget -y
wget https://artiya4u.keybase.pub/TA-lib/ta-lib-0.4.0-src.tar.gz
tar -xvf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install

patter001

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, 
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A 
# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT 
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF 
# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE 
# OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

"""
To use, import in to your file, and the main thing you want is run the *safe_launch_ibc* function
pass in your path to your start script (code assumes LOG directory is in the same directory), pass

arsalanaf

from btgym import BTgymEnv
import IPython.display as Display
import PIL.Image as Image
from gym import spaces


import gym
import numpy as np
import random

tg12

#the following code takes a list such as
#[1,1,2,6,8,5,5,7,8,8,1,1,4,5,5,0,0,0,1,1,4,4,5,1,3,3,4,5,4,1,1]
#with states labeled as successive integers starting with 0
#and returns a transition matrix, M,
#where M[i][j] is the probability of transitioning from i to j

def transition_matrix(transitions):
    n = 1+ max(transitions) #number of states

    M = [[0]*n for _ in range(n)]

gabeweaver

/*
This example was built using standard create-react-app out of the box with no modifications or ejections
to the underlying scripts. 
  
In this example, i'm using Google as a social provider configured within the Cognito User Pool.

Each step also represents a file, so you can see how I've chosen to organize stuff...you can do it however
you'd like so long as you follow the basic flow (which may or may not be the official way....but its what I found that works.
The docs are pretty horrible)
  

robcarver17

# Gist example of IB wrapper ...
#
# Download API from http://interactivebrokers.github.io/#
#
# Install python API code /IBJts/source/pythonclient $ python3 setup.py install
#
# Note: The test cases, and the documentation refer to a python package called IBApi,
#    but the actual package is called ibapi. Go figure.
#
# Get the latest version of the gateway:

yashpatel5400

import gym
import numpy as np
import random
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam

from collections import deque

class DQN: