csetzkorn

from numpy import array
from keras.preprocessing.text import one_hot
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding

# see also: https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/

csetzkorn

# Define the sim_integrate function
def sim_integrate(func, xmin, xmax, sims):
    x = np.random.uniform(xmin, xmax, sims)
    y = np.random.uniform(min(min(func(x)), 0), max(func(x)), sims)
    area = (max(y) - min(y))*(xmax-xmin)
    result = area * sum(abs(y) < abs(func(x)))/sims
    return result

# Call the sim_integrate function and print results
result = sim_integrate(func = lambda x: x*np.exp(x), xmin = 0, xmax = 1, sims = 50)

csetzkorn

# Leave one observation out to get the jackknife sample and store the median length
median_lengths = []
for i in range(n):
    jk_sample = wrench_lengths[index != i]
    median_lengths.append(np.median(jk_sample))

median_lengths = np.array(median_lengths)

# Calculate jackknife estimate and it's variance
jk_median_length = np.mean(median_lengths)

csetzkorn

rsquared_boot, coefs_boot, sims = [], [], 1000
reg_fit = sm.OLS(df['y'], df.iloc[:,1:]).fit()

# Run 1K iterations
for i in range(sims):
    # First create a bootstrap sample with replacement with n=df.shape[0]
    bootstrap = df.sample(n=df.shape[0], replace=True)
    # Fit the regression and append the r square to rsquared_boot
    rsquared_boot.append(sm.OLS(bootstrap['y'],bootstrap.iloc[:,1:]).fit().rsquared)

csetzkorn

library(dplyr)
library(ggplot2)

setwd('D:\\ToyData')

OrginalData <- read.table("https://s3.amazonaws.com/christiandata887342ac-a3ce-4600-94d0-9092f4a6bd20/IrisTabSepData/IrisData.txt",
                   header = TRUE, sep = "\t")

head(OrginalData)

csetzkorn

library(dplyr)
library(ggplot2)

setwd('D:\\ToyData')

OrginalData <- read.table("IrisData.txt",
                   header = TRUE, sep = "\t")

SubsetData <- subset(OrginalData, select = c(
#"SepalLength"

csetzkorn

import os
from scipy.misc import imread
from scipy.linalg import norm
from scipy import sum, average


def compare_images(img1, img2):
    # normalize to compensate for exposure difference, this may be unnecessary
    # consider disabling it
    img1 = normalize(img1)

csetzkorn

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

csetzkorn

import pandas as pd
from fbprophet import Prophet

import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

df = pd.read_csv('D:/PyCharmProjects/Prophet/Data/AirPassengers.csv')
df['Month'] = pd.DatetimeIndex(df['Month'])
#Prophet also imposes the strict condition that the input columns be named ds (the time column) and y (the metric column)
df = df.rename(columns={'Month': 'ds',

csetzkorn

library(dplyr)
library(kohonen)

setwd('C:\\Users\\Christian\\Source\\Repos\\RClusteringMixedDataPam')

OrginalData <- read.table("IrisData.txt",
                   header = TRUE, sep = "\t")

SubsetData <- subset(OrginalData, select = c("SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
#TrainingMatrix <- as.matrix(scale(SubsetData))