kingjr

import sys
import os
import os
import numpy as np
import torch
import matplotlib.pyplot as plt
sys.path.append(os.path.abspath('./sentence_embedding'))

from utils import load_model, build_XLM_dictionary  # noqa

kingjr

"""Fit sigmoid with parallel seeds to find global minimum.
"""
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from scipy.stats import pearsonr, wilcoxon
from sklearn.base import BaseEstimator
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LinearRegression

kingjr

"""Fit sigmoid with parallel seeds to find global minimum.
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn.base import BaseEstimator
import torch
import torch.nn as nn


class SigmoidRegression(BaseEstimator):

kingjr

kingjr

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-10, 10)
y = np.cos(x) / 2. + .5
z = np.sin(x) / 20. + .5


def jr_transform(x, baseline=.50, scaling=100):
    x = x - baseline
    above = np.log(scaling*x*(x>0)+1)

kingjr

"""
sudo apt-get install python-dev graphviz libgraphviz-dev pkg-config
pip install pygraphviz
"""
%matplotlib inline
import numpy as np
import os
import networkx as nx
from networkx.drawing.nx_agraph import to_agraph
from IPython.display import Image

kingjr

import numpy as np
from sklearn.linear_model import RidgeCV
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_predict, KFold


def correlation(x, y):
    a = (x - x.mean(0)) / x.std(0)
    b = (y - y.mean(0)) / y.std(0)

kingjr

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kingjr

from googletrans import Translator
from itertools import product
from pandas import DataFrame, read_csv
import numpy as np
import string
import time
import os

# Get google translator object
translator = Translator()

kingjr

"""
Wang and Wong 2006 perceptual decision making model

adapted from  https://github.com/xjwanglab/book/blob/b0a8e775a9f687cefbcbb4fcbe3fc19b6343bd70/wong2006/wong2006.py  # noqa
"""
import numpy as np
import matplotlib.pyplot as plt


def run(coherence=.3):