Key Word(s): Matplotlib, Simple Linear Regression, kNN, array reshape, sklearn, statsmodels
CS109A Introduction to Data Science
Lab 3: plotting, K-NN Regression, Simple Linear Regression¶
Harvard University
Fall 2019
Instructors: Pavlos Protopapas, Kevin Rader, and Chris Tanner
Material prepared by: David Sondak, Will Claybaugh, Pavlos Protopapas, and Eleni Kaxiras.
Extended Edition¶
Same as the one done in class with the following additions/clarifications:
- I added another example to illustrate the difference between
.iloc
and.loc
inpandas
-- > here - I added some notes on why we are adding a constant in our linear regression model --> here
- How to run the solutions: Uncomment the following line and run the cell:
# %load solutions/knn_regression.py
This will bring up the code in the cell but WILL NOT RUN it. You need to run the cell again in order to actually run the code
#RUN THIS CELL
import requests
from IPython.core.display import HTML
styles = requests.get("https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/cs109.css").text
HTML(styles)
Learning Goals¶
By the end of this lab, you should be able to:
- Review
numpy
including 2-D arrays and understand array reshaping - Use
matplotlib
to make plots - Feel comfortable with simple linear regression
- Feel comfortable with $k$ nearest neighbors
This lab corresponds to lectures 4 and 5 and maps on to homework 2 and beyond.
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import time
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
#import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
# Displays the plots for us.
%matplotlib inline
# Use this as a variable to load solutions: %load PATHTOSOLUTIONS/exercise1.py. It will be substituted in the code
# so do not worry if it disappears after you run the cell.
PATHTOSOLUTIONS = 'solutions'
1 - Review of the numpy
Python library¶
In lab1 we learned about the numpy
library (documentation) and its fast array structure, called the numpy array
.
# import numpy
import numpy as np
# make an array
my_array = np.array([1,4,9,16])
my_array
print(f'Size of my array: {my_array.size}, or length of my array: {len(my_array)}')
print (f'Shape of my array: {my_array.shape}')
Notice the way the shape appears in numpy arrays¶
- For a 1D array, .shape returns a tuple with 1 element (n,)
- For a 2D array, .shape returns a tuple with 2 elements (n,m)
- For a 3D array, .shape returns a tuple with 3 elements (n,m,p)
# How to reshape a 1D array to a 2D
my_array.reshape(-1,2)
Numpy arrays support the same operations as lists! Below we slice and iterate.
print("array[2:4]:", my_array[2:4]) # A slice of the array
# Iterate over the array
for ele in my_array:
print("element:", ele)
Remember numpy
gains a lot of its efficiency from being strongly typed (all elements are of the same type, such as integer or floating point). If the elements of an array are of a different type, numpy
will force them into the same type (the longest in terms of bytes)
mixed = np.array([1, 2.3, 'eleni', True])
print(type(1), type(2.3), type('eleni'), type(True))
mixed # all elements will become strings
Next, we push ahead to two-dimensional arrays and begin to dive into some of the deeper aspects of numpy
.
# create a 2d-array by handing a list of lists
my_array2d = np.array([ [1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]
])
my_array2d
Array Slicing (a reminder...)¶
Numpy arrays can be sliced, and can be iterated over with loops. Below is a schematic illustrating slicing two-dimensional arrays.
Notice that the list slicing syntax still works!
array[2:,3]
says "in the array, get rows 2 through the end, column 3]"
array[3,:]
says "in the array, get row 3, all columns".
Pandas Slicing (a reminder...)¶
.iloc
is by position (position is unique), .loc
is by label (label is not unique)
# import cast dataframe
cast = pd.read_csv('../data/cast.csv', encoding='utf_8')
cast.head()
# get me rows 10 to 13 (python slicing style : exclusive of end)
cast.iloc[10:13]
# get me columns 0 to 2 but all rows - use head()
cast.iloc[:, 0:2].head()
# get me rows 10 to 13 AND only columns 0 to 2
cast.iloc[10:13, 0:2]
# COMPARE: get me rows 10 to 13 (pandas slicing style : inclusive of end)
cast.loc[10:13]
# give me columns 'year' and 'type' by label but only for rows 5 to 10
cast.loc[5:10,['year','type']]
Another example of positioning with .iloc
and loc
¶
Look at the following data frame. It is a bad example because we have duplicate values for the index but that is legal in pandas. It's just a bad practice and we are doing it to illustrate the difference between positioning with .iloc
and loc
. To keep rows unique, though, internally, pandas
has its own index which in this dataframe runs from 0
to 2
.
index = ['A', 'Z', 'A']
famous = pd.DataFrame({'Elton': ['singer', 'Candle in the wind', 'male'],
'Maraie': ['actress' , 'Do not know', 'female'],
'num': np.random.randn(3)}, index=index)
famous
# accessing elements by label can bring up duplicates!!
famous.loc['A'] # since we want all rows is the same as famous.loc['A',:]
# accessing elements by position is unique - brings up only one row
famous.iloc[1]
2 - Plotting with matplotlib and beyond¶
matplotlib
is a very powerful python
library for making scientific plots.
We will not focus too much on the internal aspects of matplotlib
in today's lab. There are many excellent tutorials out there for matplotlib
. For example,
Conveying your findings convincingly is an absolutely crucial part of any analysis. Therefore, you must be able to write well and make compelling visuals. Creating informative visuals is an involved process and we won't cover that in this lab. However, part of creating informative data visualizations means generating readable figures. If people can't read your figures or have a difficult time interpreting them, they won't understand the results of your work. Here are some non-negotiable commandments for any plot:
- Label $x$ and $y$ axes
- Axes labels should be informative
- Axes labels should be large enough to read
- Make tick labels large enough
- Include a legend if necessary
- Include a title if necessary
- Use appropriate line widths
- Use different line styles for different lines on the plot
- Use different markers for different lines
There are other important elements, but that list should get you started on your way.
We will work with matplotlib
and seaborn
for plotting in this class. matplotlib
is a very powerful python
library for making scientific plots. seaborn
is a little more specialized in that it was developed for statistical data visualization. We will cover some seaborn
later in class. In the meantime you can look at the seaborn documentation
First, let's generate some data.
Let's plot some functions¶
We will use the following three functions to make some plots:
- Logistic function: \begin{align*} f\left(z\right) = \dfrac{1}{1 + be^{-az}} \end{align*} where $a$ and $b$ are parameters.
- Hyperbolic tangent: \begin{align*} g\left(z\right) = b\tanh\left(az\right) + c \end{align*} where $a$, $b$, and $c$ are parameters.
- Rectified Linear Unit:
\begin{align*}
h\left(z\right) =
\left{
\right. \end{align*} where $\epsilon < 0$ is a small, positive parameter.\begin{array}{lr} z, \quad z > 0 \\ \epsilon z, \quad z\leq 0 \end{array}
You are given the code for the first two functions. Notice that $z$ is passed in as a numpy
array and that the functions are returned as numpy
arrays. Parameters are passed in as floats.
You should write a function to compute the rectified linear unit. The input should be a numpy
array for $z$ and a positive float for $\epsilon$.
import numpy as np
def logistic(z: np.ndarray, a: float, b: float) -> np.ndarray:
""" Compute logistic function
Inputs:
a: exponential parameter
b: exponential prefactor
z: numpy array; domain
Outputs:
f: numpy array of floats, logistic function
"""
den = 1.0 + b * np.exp(-a * z)
return 1.0 / den
def stretch_tanh(z: np.ndarray, a: float, b: float, c: float) -> np.ndarray:
""" Compute stretched hyperbolic tangent
Inputs:
a: horizontal stretch parameter (a>1 implies a horizontal squish)
b: vertical stretch parameter
c: vertical shift parameter
z: numpy array; domain
Outputs:
g: numpy array of floats, stretched tanh
"""
return b * np.tanh(a * z) + c
def relu(z: np.ndarray, eps: float = 0.01) -> np.ndarray:
""" Compute rectificed linear unit
Inputs:
eps: small positive parameter
z: numpy array; domain
Outputs:
h: numpy array; relu
"""
return np.fmax(z, eps * z)
Now let's make some plots. First, let's just warm up and plot the logistic function.
x = np.linspace(-5.0, 5.0, 100) # Equally spaced grid of 100 pts between -5 and 5
f = logistic(x, 1.0, 1.0) # Generate data
plt.plot(x, f)
plt.xlabel('x')
plt.ylabel('f')
plt.title('Logistic Function')
plt.grid(True)
Figures with subplots¶
Let's start thinking about the plots as objects. We have the figure
object which is like a matrix of smaller plots named axes
. You can use array notation when handling it.
fig, ax = plt.subplots(1,1) # Get figure and axes objects
ax.plot(x, f) # Make a plot
# Create some labels
ax.set_xlabel('x')
ax.set_ylabel('f')
ax.set_title('Logistic Function')
# Grid
ax.grid(True)
Wow, it's exactly the same plot! Notice, however, the use of ax.set_xlabel()
instead of plt.xlabel()
. The difference is tiny, but you should be aware of it. I will use this plotting syntax from now on.
What else do we need to do to make this figure better? Here are some options:
- Make labels bigger!
- Make line fatter
- Make tick mark labels bigger
- Make the grid less pronounced
- Make figure bigger
Let's get to it.
fig, ax = plt.subplots(1,1, figsize=(10,6)) # Make figure bigger
# Make line plot
ax.plot(x, f, lw=4)
# Update ticklabel size
ax.tick_params(labelsize=24)
# Make labels
ax.set_xlabel(r'$x$', fontsize=24) # Use TeX for mathematical rendering
ax.set_ylabel(r'$f(x)$', fontsize=24) # Use TeX for mathematical rendering
ax.set_title('Logistic Function', fontsize=24)
ax.grid(True, lw=1.5, ls='--', alpha=0.75)
Notice:
lw
stands forlinewidth
. We could also writeax.plot(x, f, linewidth=4)
ls
stands forlinestyle
.alpha
stands for transparency.
The only thing remaining to do is to change the $x$ limits. Clearly these should go from $-5$ to $5$.
#fig.savefig('logistic.png')
# Put this in a markdown cell and uncomment this to check what you saved.
# ![](../images/logistic.png)
Resources¶
If you want to see all the styles available, please take a look at the documentation.
We haven't discussed it yet, but you can also put a legend on a figure. You'll do that in the next exercise. Here are some additional resources:
ax.legend(loc='best', fontsize=24);
Do the following:
- Make a figure with the logistic function, hyperbolic tangent, and rectified linear unit.
- Use different line styles for each plot
- Put a legend on your figure
Here's an example of a figure:
# your code here
# First get the data
f = logistic(x, 2.0, 1.0)
g = stretch_tanh(x, 2.0, 0.5, 0.5)
h = relu(x)
fig, ax = plt.subplots(1,1, figsize=(10,6)) # Create figure object
# Make actual plots
# (Notice the label argument!)
ax.plot(x, f, lw=4, ls='-', label=r'$L(x;1)$')
ax.plot(x, g, lw=4, ls='--', label=r'$\tanh(2x)$')
ax.plot(x, h, lw=4, ls='-.', label=r'$relu(x; 0.01)$')
# Make the tick labels readable
ax.tick_params(labelsize=24)
# Set axes limits to make the scale nice
ax.set_xlim(x.min(), x.max())
ax.set_ylim(h.min(), 1.1)
# Make readable labels
ax.set_xlabel(r'$x$', fontsize=24)
ax.set_ylabel(r'$h(x)$', fontsize=24)
ax.set_title('Activation Functions', fontsize=24)
# Set up grid
ax.grid(True, lw=1.75, ls='--', alpha=0.75)
# Put legend on figure
ax.legend(loc='best', fontsize=24);
fig.savefig('../images/nice_plots.png')
These figures look nice in the plot and it makes sense for comparison. Now let's put the 3 different figures in separate plots.
- Make a separate plot for each figure and line them up on the same row.
# your code here
# %load solutions/three_subplots.py
- Make a grid of 2 x 3 separate plots, 3 will be empty. Just plot the functions and do not worry about cosmetics. We just want you ro see the functionality.
# your code here
%load solutions/six_subplots.py
3 - Simple Linear Regression¶
Linear regression and its many extensions are a workhorse of the statistics and data science community, both in application and as a reference point for other models. Most of the major concepts in machine learning can be and often are discussed in terms of various linear regression models. Thus, this section will introduce you to building and fitting linear regression models and some of the process behind it, so that you can 1) fit models to data you encounter 2) experiment with different kinds of linear regression and observe their effects 3) see some of the technology that makes regression models work.
Linear regression with a toy dataset¶
We first examine a toy problem, focusing our efforts on fitting a linear model to a small dataset with three observations. Each observation consists of one predictor $x_i$ and one response $y_i$ for $i = 1, 2, 3$,
\begin{align*} (x , y) = \{(x_1, y_1), (x_2, y_2), (x_3, y_3)\}. \end{align*}To be very concrete, let's set the values of the predictors and responses.
\begin{equation*} (x , y) = \{(1, 2), (2, 2), (3, 4)\} \end{equation*}There is no line of the form $\beta_0 + \beta_1 x = y$ that passes through all three observations, since the data are not collinear. Thus our aim is to find the line that best fits these observations in the least-squares sense, as discussed in lecture.
- Make two numpy arrays out of this data, x_train and y_train
- Check the dimentions of these arrays
- Try to reshape them into a different shape
- Make points into a very simple scatterplot
- Make a better scatterplot
# your code here
# solution
x_train = np.array([1,2,3])
y_train = np.array([2,3,6])
type(x_train)
x_train.shape
x_train = x_train.reshape(3,1)
x_train.shape
# %load solutions/simple_scatterplot.py
# Make a simple scatterplot
plt.scatter(x_train,y_train)
# check dimensions
print(x_train.shape,y_train.shape)
# %load solutions/nice_scatterplot.py
def nice_scatterplot(x, y, title):
# font size
f_size = 18
# make the figure
fig, ax = plt.subplots(1,1, figsize=(8,5)) # Create figure object
# set axes limits to make the scale nice
ax.set_xlim(np.min(x)-1, np.max(x) + 1)
ax.set_ylim(np.min(y)-1, np.max(y) + 1)
# adjust size of tickmarks in axes
ax.tick_params(labelsize = f_size)
# remove tick labels
ax.tick_params(labelbottom=False, bottom=False)
# adjust size of axis label
ax.set_xlabel(r'$x$', fontsize = f_size)
ax.set_ylabel(r'$y$', fontsize = f_size)
# set figure title label
ax.set_title(title, fontsize = f_size)
# you may set up grid with this
ax.grid(True, lw=1.75, ls='--', alpha=0.15)
# make actual plot (Notice the label argument!)
#ax.scatter(x, y, label=r'$my points$')
#ax.scatter(x, y, label='$my points$')
ax.scatter(x, y, label=r'$my\,points$')
ax.legend(loc='best', fontsize = f_size);
return ax
nice_scatterplot(x_train, y_train, 'hello nice plot')
Formulae¶
Linear regression is special among the models we study because it can be solved explicitly. While most other models (and even some advanced versions of linear regression) must be solved itteratively, linear regression has a formula where you can simply plug in the data.
For the single predictor case it is: \begin{align} \beta_1 &= \frac{\sum_{i=1}^n{(x_i-\bar{x})(y_i-\bar{y})}}{\sum_{i=1}^n{(x_i-\bar{x})^2}}\\ \beta_0 &= \bar{y} - \beta_1\bar{x}\ \end{align}
Where $\bar{y}$ and $\bar{x}$ are the mean of the y values and the mean of the x values, respectively.
Building a model from scratch¶
In this part, we will solve the equations for simple linear regression and find the best fit solution to our toy problem.
The snippets of code below implement the linear regression equations on the observed predictors and responses, which we'll call the training data set. Let's walk through the code.
We have to reshape our arrrays to 2D. We will see later why.
- make an array with shape (2,3)
- reshape it to a size that you want
# your code here
#solution
xx = np.array([[1,2,3],[4,6,8]])
xxx = xx.reshape(-1,2)
xxx.shape
# Reshape to be a proper 2D array
x_train = x_train.reshape(x_train.shape[0], 1)
y_train = y_train.reshape(y_train.shape[0], 1)
print(x_train.shape)
# first, compute means
y_bar = np.mean(y_train)
x_bar = np.mean(x_train)
# build the two terms
numerator = np.sum( (x_train - x_bar)*(y_train - y_bar) )
denominator = np.sum((x_train - x_bar)**2)
print(numerator.shape, denominator.shape) #check shapes
- Why the empty brackets? (The numerator and denominator are scalars, as expected.)
#slope beta1
beta_1 = numerator/denominator
#intercept beta0
beta_0 = y_bar - beta_1*x_bar
print("The best-fit line is {0:3.2f} + {1:3.2f} * x".format(beta_0, beta_1))
print(f'The best fit is {beta_0}')
Turn the code from the above cells into a function called simple_linear_regression_fit
, that inputs the training data and returns beta0
and beta1
.
To do this, copy and paste the code from the above cells below and adjust the code as needed, so that the training data becomes the input and the betas become the output.
def simple_linear_regression_fit(x_train: np.ndarray, y_train: np.ndarray) -> np.ndarray:
return
Check your function by calling it with the training data from above and printing out the beta values.
# Your code here
# %load solutions/simple_linear_regression_fit.py
def simple_linear_regression_fit(x_train: np.ndarray, y_train: np.ndarray) -> np.ndarray:
"""
Inputs:
x_train: a (num observations by 1) array holding the values of the predictor variable
y_train: a (num observations by 1) array holding the values of the response variable
Returns:
beta_vals: a (num_features by 1) array holding the intercept and slope coeficients
"""
# Check input array sizes
if len(x_train.shape) < 2:
print("Reshaping features array.")
x_train = x_train.reshape(x_train.shape[0], 1)
if len(y_train.shape) < 2:
print("Reshaping observations array.")
y_train = y_train.reshape(y_train.shape[0], 1)
# first, compute means
y_bar = np.mean(y_train)
x_bar = np.mean(x_train)
# build the two terms
numerator = np.sum( (x_train - x_bar)*(y_train - y_bar) )
denominator = np.sum((x_train - x_bar)**2)
#slope beta1
beta_1 = numerator/denominator
#intercept beta0
beta_0 = y_bar - beta_1*x_bar
return np.array([beta_0,beta_1])
- Let's run this function and see the coefficients
x_train = np.array([1 ,2, 3])
y_train = np.array([2, 2, 4])
betas = simple_linear_regression_fit(x_train, y_train)
beta_0 = betas[0]
beta_1 = betas[1]
print("The best-fit line is {0:8.6f} + {1:8.6f} * x".format(beta_0, beta_1))
- Do the values of
beta0
andbeta1
seem reasonable? - Plot the training data using a scatter plot.
- Plot the best fit line with
beta0
andbeta1
together with the training data.
# Your code here
# %load solutions/best_fit_scatterplot.py
fig_scat, ax_scat = plt.subplots(1,1, figsize=(10,6))
# Plot best-fit line
x_train = np.array([[1, 2, 3]]).T
best_fit = beta_0 + beta_1 * x_train
ax_scat.scatter(x_train, y_train, s=300, label='Training Data')
ax_scat.plot(x_train, best_fit, ls='--', label='Best Fit Line')
ax_scat.set_xlabel(r'$x_{train}$')
ax_scat.set_ylabel(r'$y$');
The values of beta0
and beta1
seem roughly reasonable. They capture the positive correlation. The line does appear to be trying to get as close as possible to all the points.
4 - Building a model with statsmodels
and sklearn
¶
Now that we can concretely fit the training data from scratch, let's learn two python
packages to do it all for us:
Our goal is to show how to implement simple linear regression with these packages. For an important sanity check, we compare the $\beta$ values from statsmodels
and sklearn
to the $\beta$ values that we found from above with our own implementation.
For the purposes of this lab, statsmodels
and sklearn
do the same thing. More generally though, statsmodels
tends to be easier for inference [finding the values of the slope and intercept and dicussing uncertainty in those values], whereas sklearn
has machine-learning algorithms and is better for prediction [guessing y values for a given x value]. (Note that both packages make the same guesses, it's just a question of which activity they provide more support for.
Note: statsmodels
and sklearn
are different packages! Unless we specify otherwise, you can use either one.
Why do we need to add a constant in our simple linear regression model?¶
Let's say we a data set of two obsevations with one predictor and one response variable each. We would then have the following two equations if we run a simple linear regression model. $$y_1=\beta_0 + \beta_1*x_1$$ $$y_2=\beta_0 + \beta_1*x_2$$
For simplicity and calculation efficiency we want to "absorb" the constant $b_0$ into an array with $b_1$ so we have only multiplication. To do this we introduce the constant ${x}^0=1$
$$y_1=\beta_0*{x_1}^0 + \beta_1*x_1$$ $$y_2=\beta_0 * {x_2}^0 + \beta_1*x_2$$
That becomes:
$$y_1=\beta_0*1 + \beta_1*x_1$$ $$y_2=\beta_0 * 1 + \beta_1*x_2$$
In matrix notation:
$$ \left [ \begin{array}{c} y_1 \\ y_2 \\ \end{array} \right] = \left [ \begin{array}{cc} 1& x_1 \\ 1 & x_2 \\ \end{array} \right] \cdot \left [ \begin{array}{c} \beta_0 \\ \beta_1 \\ \end{array} \right] $$sklearn
adds the constant for us where in statsmodels
we need to explicitly add it using sm.add_constant
Below is the code for statsmodels
. Statsmodels
does not by default include the column of ones in the $X$ matrix, so we include it manually with sm.add_constant
.
import statsmodels.api as sm
# create the X matrix by appending a column of ones to x_train
X = sm.add_constant(x_train)
# this is the same matrix as in our scratch problem!
print(X)
# build the OLS model (ordinary least squares) from the training data
toyregr_sm = sm.OLS(y_train, X)
# do the fit and save regression info (parameters, etc) in results_sm
results_sm = toyregr_sm.fit()
# pull the beta parameters out from results_sm
beta0_sm = results_sm.params[0]
beta1_sm = results_sm.params[1]
print(f'The regression coef from statsmodels are: beta_0 = {beta0_sm:8.6f} and beta_1 = {beta1_sm:8.6f}')
Besides the beta parameters, results_sm
contains a ton of other potentially useful information.
import warnings
warnings.filterwarnings('ignore')
print(results_sm.summary())
Now let's turn our attention to the sklearn
library.
from sklearn import linear_model
# build the least squares model
toyregr = linear_model.LinearRegression()
# save regression info (parameters, etc) in results_skl
results = toyregr.fit(x_train, y_train)
# pull the beta parameters out from results_skl
beta0_skl = toyregr.intercept_
beta1_skl = toyregr.coef_[0]
print("The regression coefficients from the sklearn package are: beta_0 = {0:8.6f} and beta_1 = {1:8.6f}".format(beta0_skl, beta1_skl))
We should feel pretty good about ourselves now, and we're ready to move on to a real problem!
The scikit-learn
library and the shape of things¶
Before diving into a "real" problem, let's discuss more of the details of sklearn
.
Scikit-learn
is the main Python
machine learning library. It consists of many learners which can learn models from data, as well as a lot of utility functions such as train_test_split()
.
Use the following to add the library into your code:
import sklearn
In scikit-learn
, an estimator is a Python object that implements the methods fit(X, y)
and predict(T)
Let's see the structure of scikit-learn
needed to make these fits. fit()
always takes two arguments:
estimator.fit(Xtrain, ytrain)
We will consider two estimators in this lab: LinearRegression
and KNeighborsRegressor
.
It is very important to understand that Xtrain
must be in the form of a 2x2 array with each row corresponding to one sample, and each column corresponding to the feature values for that sample.
ytrain
on the other hand is a simple array of responses. These are continuous for regression problems.
Practice with sklearn
and a real dataset¶
We begin by loading up the mtcars
dataset. This data was extracted from the 1974 Motor Trend US magazine, and comprises of fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). We will load this data to a dataframe with 32 observations on 11 (numeric) variables. Here is an explanation of the features:
mpg
is Miles/(US) galloncyl
is Number of cylinders,disp
is Displacement (cu.in.),hp
is Gross horsepower,drat
is Rear axle ratio,wt
is the Weight (1000 lbs),qsec
is 1/4 mile time,vs
is Engine (0 = V-shaped, 1 = straight),am
is Transmission (0 = automatic, 1 = manual),gear
is the Number of forward gears,carb
is Number of carburetors.
import pandas as pd
#load mtcars
dfcars = pd.read_csv("../data/mtcars.csv")
dfcars.head()
# Fix the column title
dfcars = dfcars.rename(columns={"Unnamed: 0":"car name"})
dfcars.head()
dfcars.shape
Searching for values: how many cars have 4 gears?¶
len(dfcars[dfcars.gear == 4].drop_duplicates(subset='car name', keep='first'))
Next, let's split the dataset into a training set and test set.
# split into training set and testing set
from sklearn.model_selection import train_test_split
#set random_state to get the same split every time
traindf, testdf = train_test_split(dfcars, test_size=0.2, random_state=42)
# testing set is around 20% of the total data; training set is around 80%
print("Shape of full dataset is: {0}".format(dfcars.shape))
print("Shape of training dataset is: {0}".format(traindf.shape))
print("Shape of test dataset is: {0}".format(testdf.shape))
Now we have training and test data. We still need to select a predictor and a response from this dataset. Keep in mind that we need to choose the predictor and response from both the training and test set. You will do this in the exercises below. However, we provide some starter code for you to get things going.
traindf.head()
# Extract the response variable that we're interested in
y_train = traindf.mpg
y_train
Use slicing to get the same vector y_train
Now, notice the shape of y_train
.
y_train.shape, type(y_train)
Array reshape¶
This is a 1D array as should be the case with the Y array. Remember, sklearn
requires a 2D array only for the predictor array. You will have to pay close attention to this in the exercises later. Sklearn
doesn't care too much about the shape of y_train
.
The whole reason we went through that whole process was to show you how to reshape your data into the correct format.
IMPORTANT: Remember that your response variable ytrain
can be a vector but your predictor variable xtrain
must be an array!
5 - Example: Simple linear regression with automobile data¶
We will now use sklearn
to predict automobile mileage per gallon (mpg) and evaluate these predictions. We already loaded the data and split them into a training set and a test set.
We need to choose the variables that we think will be good predictors for the dependent variable mpg
.
- Pick one variable to use as a predictor for simple linear regression. Discuss your reasons with the person next to you.
- Justify your choice with some visualizations.
- Is there a second variable you'd like to use? For example, we're not doing multiple linear regression here, but if we were, is there another variable you'd like to include if we were using two predictors?
x_wt = dfcars.wt
x_wt.shape
# Your code here
# %load solutions/cars_simple_EDA.py
- Use
sklearn
to fit the training data using simple linear regression. - Use the model to make mpg predictions on the test set.
- Plot the data and the prediction.
- Print out the mean squared error for the training set and the test set and compare.
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
dfcars = pd.read_csv("../data/mtcars.csv")
dfcars = dfcars.rename(columns={"Unnamed: 0":"name"})
dfcars.head()
traindf, testdf = train_test_split(dfcars, test_size=0.2, random_state=42)
y_train = np.array(traindf.mpg)
X_train = np.array(traindf.wt)
X_train = X_train.reshape(X_train.shape[0], 1)
y_test = np.array(testdf.mpg)
X_test = np.array(testdf.wt)
X_test = X_test.reshape(X_test.shape[0], 1)
# Let's take another look at our data
dfcars.head()
# And out train and test sets
y_train.shape, X_train.shape
y_test.shape, X_test.shape
#create linear model
regression = LinearRegression()
#fit linear model
regression.fit(X_train, y_train)
predicted_y = regression.predict(X_test)
r2 = regression.score(X_test, y_test)
print(f'R^2 = {r2:.5}')
print(regression.score(X_train, y_train))
print(mean_squared_error(predicted_y, y_test))
print(mean_squared_error(y_train, regression.predict(X_train)))
print('Coefficients: \n', regression.coef_[0], regression.intercept_)
fig, ax = plt.subplots(1,1, figsize=(10,6))
ax.plot(y_test, predicted_y, 'o')
grid = np.linspace(np.min(dfcars.mpg), np.max(dfcars.mpg), 100)
ax.plot(grid, grid, color="black") # 45 degree line
ax.set_xlabel("actual y")
ax.set_ylabel("predicted y")
fig1, ax1 = plt.subplots(1,1, figsize=(10,6))
ax1.plot(dfcars.wt, dfcars.mpg, 'o')
xgrid = np.linspace(np.min(dfcars.wt), np.max(dfcars.wt), 100)
ax1.plot(xgrid, regression.predict(xgrid.reshape(100, 1)))
6 - $k$-nearest neighbors¶
Now that you're familiar with sklearn
, you're ready to do a KNN regression.
Sklearn's regressor is called sklearn.neighbors.KNeighborsRegressor
. Its main parameter is the number of nearest neighbors
. There are other parameters such as the distance metric (default for 2 order is the Euclidean distance). For a list of all the parameters see the Sklearn kNN Regressor Documentation.
Let's use $5$ nearest neighbors.
# Import the library
from sklearn.neighbors import KNeighborsRegressor
# Set number of neighbors
k = 5
knnreg = KNeighborsRegressor(n_neighbors=k)
# Fit the regressor - make sure your numpy arrays are the right shape
knnreg.fit(X_train, y_train)
# Evaluate the outcome on the train set using R^2
r2_train = knnreg.score(X_train, y_train)
# Print results
print(f'kNN model with {k} neighbors gives R^2 on the train set: {r2_train:.5}')
knnreg.predict(X_test)
Calculate and print the $R^{2}$ score on the test set
# Your code here
Not so good? Lets vary the number of neighbors and see what we get.
# Make our lives easy by storing the different regressors in a dictionary
regdict = {}
# Make our lives easier by entering the k values from a list
k_list = [1, 2, 4, 15]
# Do a bunch of KNN regressions
for k in k_list:
knnreg = KNeighborsRegressor(n_neighbors=k)
knnreg.fit(X_train, y_train)
# Store the regressors in a dictionary
regdict[k] = knnreg
# Print the dictionary to see what we have
regdict
Now let's plot all the k values in same plot.
fig, ax = plt.subplots(1,1, figsize=(10,6))
ax.plot(dfcars.wt, dfcars.mpg, 'o', label="data")
xgrid = np.linspace(np.min(dfcars.wt), np.max(dfcars.wt), 100)
# let's unpack the dictionary to its elements (items) which is the k and Regressor
for k, regressor in regdict.items():
predictions = regressor.predict(xgrid.reshape(-1,1))
ax.plot(xgrid, predictions, label="{}-NN".format(k))
ax.legend();
Explain what you see in the graph. Hint Notice how the $1$-NN goes through every point on the training set but utterly fails elsewhere.
Lets look at the scores on the training set.
ks = range(1, 15) # Grid of k's
scores_train = [] # R2 scores
for k in ks:
# Create KNN model
knnreg = KNeighborsRegressor(n_neighbors=k)
# Fit the model to training data
knnreg.fit(X_train, y_train)
# Calculate R^2 score
score_train = knnreg.score(X_train, y_train)
scores_train.append(score_train)
# Plot
fig, ax = plt.subplots(1,1, figsize=(12,8))
ax.plot(ks, scores_train,'o-')
ax.set_xlabel(r'$k$')
ax.set_ylabel(r'$R^{2}$')
- Why do we get a perfect $R^2$ at k=1 for the training set?
- Make the same plot as above on the test set.
- What is the best $k$?
# Your code here
# %load solutions/knn_regression.py
# solution to previous exercise
r2_test = knnreg.score(X_test, y_test)
print(f'kNN model with {k} neighbors gives R^2 on the test set: {r2_test:.5}')


Determines where the scrollbar should be drawn - on the left side or on the right side of the grid.
Note: This option cannot be toggled via grid.setOptions(). It must be set at initialization.
grid.setOptions()
(string): Default right.
(string)
right
{scrollbarPosition: "left"}


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There's so much room for activities!
@sergiolucas: Most rain in the last two days: 85mm Gabo Island (March)
Frontend Developer
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Never give up


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Donation
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I'll use it for buying books to learn more about programming and create better tools.


CHAPTER 448
DENTISTRY
Section
448-1 Dentistry defined; exempted practices
448-1.5 Lead apron during x-ray; required
448-2 Practice without license prohibited
448-3 Practice by unlicensed employee prohibited; penalty
448-4 Fraudulent advertising
448-5 Board of examiners; appointment
448-6 Powers and duties, meetings
448-7 Fees
448-8 Repealed
448-8.5 Continuing education requirements
448-9 Application for licensure
448-9.4 American Board of Dental Examiners (ADEX) examination;
regional examinations
448-9.5 Repealed
448-9.6 Community service license
448-10 Repealed
448-11 Investigations and hearings
448-12 Temporary license
448-13 Certificate, evidence
448-14 Repealed
448-14.5 Prohibition on ownership and interference
448-15 No corporation to practice dentistry; penalty
448-16 Duty to furnish names, etc.
448-16.5 Board of dental examiners; summary suspension
448-17 Refusal, revocation, suspension, and administrative
penalties
448-18 Hearings
448-19 Using assumed name or degree prohibited
448-20 Practicing for advertising purposes, penalty
448-21 Criminal penalties
448-22 Prosecuting officers, duty
448-23 Filing of false information; revocation of license
448-24 Remedies or penalties cumulative
448-26 Discipline based on action taken by another state or
federal agency; conditions; prohibition on practice
448-28 Notice to consumers
448-29 Administration of general anesthesia and sedation;
requirements
448-30 Inspection of facilities, equipment, and personnel
Note
Board of dental examiners; adoption of rules pertaining to anesthesia and sedation administration, no later than July 1, 2018. L 2017, c 106, §3.
Dental assistants; sunrise analysis on effects of regulation; report to 2018 legislature. L 2017, c 84, §2.
Cross References
Sunset evaluations modified, see §§26H-4 and 26H-5.
Dental health facilities; health care facilities; use of latex gloves, see §321-11.9.
Dental service corporations, see chapter 423.
Health care peer review, see chapter 671D.
Health care professionals, see chapter 451D.


There are times when you would want to check connectivity to your servers without actually login or SSH. You can use telnet on port 22. But here are few other ways:
I love the idea of open source, web-presence, communication, & making things work.
When not giving anything back to community, I enjoy photography, swimming, movies.
Have a look around, read some of my stuff.
There are times when you would want to check connectivity to your servers without actually login or SSH. You can use telnet on port 22. But here are few other ways:
Open and Free wireless is everywhere, but should you be connecting to it. The answer is NO. You shouldn.t be connecting to it, dont connect to your social networks, don't check your bank account on it unless you want somebody else to be able to sneak on it.
The solution? A virtual private network, or VPN. A VPN extends your own private network into public places, so even if you.re using Starbucks. Wi-Fi connection, your Internet browsing stays encrypted and secure.
Sometimes back I had setup ssh on raspberry pi and allowed to login from internet, obviously using the public key encyption. After few days I noticed that lot of people/systems were trying to login and failing from various different IPs. So I block them using Fail2ban. I am Not gonna talk about Fail2ban, as its completely vast topic on its own.


Class Barcode128
Generates the Code128 barcode.
Inherited Members
Namespace: FastReport.Barcode
Assembly: FastReport.OpenSource.dll
Syntax
public class Barcode128 : LinearBarcodeBase
Remarks
This barcode supports three code pages: A, B and C. You need to set appropriate code page in the barcode text, or use the auto encode feature. See the AutoEncode property for more details.
Examples
This example shows how to configure the BarcodeObject to display Code128 barcode.
BarcodeObject barcode;
...
barcode.Barcode = new Barcode128();
(barcode.Barcode as Barcode128).AutoEncode = false;
Constructors
Barcode128()
Initializes a new instance of the Barcode128 class with default settings.
Declaration
public Barcode128()
Properties
AutoEncode
Gets or sets a value that determines whether the barcode should automatically use appropriate encoding.
Declaration
public bool AutoEncode { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Remarks
You may use this property to encode data automatically. If you set it to false, you must specify the code page inside the data string. The following control codes are available:
| Sequence | Code128 control code |
|---|---|
| &A; | START A / CODE A |
| &B; | START B / CODE B |
| &C; | START C / CODE C |
Examples
The following example shows how to specify control codes:
BarcodeObject barcode;
barcode.Barcode = new Barcode128();
(barcode.Barcode as Barcode128).AutoEncode = false;
barcode.Text = "&C;1234&A;ABC";
IsNumeric
Gets the value indicating that the barcode is numeric.
Declaration
public override bool IsNumeric { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Overrides
Methods
Assign(BarcodeBase)
Assigns properties from other, similar barcode.
Declaration
public override void Assign(BarcodeBase source)
Parameters
| Type | Name | Description |
|---|---|---|
| BarcodeBase | source | Barcode object to assign properties from. |


Objective
To compile a simple C++ program to learn the process of writing and running a program.
Materials
- C++ Compiler
- Access to your computer's terminal/command-line interface
Methods
- Open the terminal for your operating system.
-
Type:
g++ --version
And press ENTER. Make sure that you get a message displaying what version of g++ you have. If not you will need to install this program
-
Create a new file named hello-world.cpp and open the file for editing in your favorite basic text editor or IDE.
Place the following code inside the file:
#include <iostream> using namespace std; int main() { cout << "Hello World!\n"; return 0; }
When you are finished, save the file
-
Next we are going to compile your human-readable code file into a computer program your computer can understand. In your terminal, navigate to the location of your hello-world.cpp file. Once there, type:
g++ hello-world.cpp
If you don't see any error messages, then congratulations! You have successfully compiled your first C++ program. The g++ command turns your human-readable text file into binary. Just to be sure, look inside the directory where your hello-world.cpp file is. You should see an additional file named a.out. This new file is the binary executable file that the computer will read to run your computer program.
-
Next, we are going to execute the binary file a.out. To do this, we simply need to type one more command. Please type:
./a.out
You should see the words print to the screen. If so, you have just completed the lab!
Conclusion
You now have a sense of the process for writing and compiling computer programs. You started by creating a file and filling it with code. Then you compiled the code into an executable. After this you ran the code and witnessed the output.


4 lessons for 90 minutes each
How Cranial growth influences the shape, the size and the position of the jaws. The influence of cranial growth on the position of the teeth.
Prior to commencement of orthodontic or orthopaedic treatment, you must screen patients for myofunctional and airway issues.
Identifying Soft Tissue Dysfunction. The face is like a book, you need to learn how to read it.
Treating mild malocclusion in the early mixed dentition Using MRC Appliances
Price for you $280 you save 40%
The required number of training hours 60 until 10/30/2022
4 lessons for 90 minutes each
The problem is Poor Craniofacial Development. How they all have a common cause.
Class I Class II and Class III explained
Snoring and Sleep Apnoea are breathing problems
How tongue and lip posture and tongue and lip function relate to joint health
Price for you $280 you save 40%
The required number of training hours 60 until 10/30/2022
4 lessons for 90 minutes each
Diagnosis of mouth breathing
Educating children and parents to the importance of establishing nasal breathing
Much of the breathing programme is delivered most efficiently by a trained educator.
The key to good growth and development of the cranium relies on good nasal breathing. Delivering a programme that supports nasal breathing is a challenge.
Price for you $280 you save 40%
The required number of training hours 60 until 10/30/2022
4 lessons for 90 minutes each
The upper dental arch can be easily expanded in the early mixed dentition. Arch expansion is to make room for the tongue to rest and function in the palate.
The Biobloc Stage 1 appliance is the ideal arch expansion appliance in many situations. Theory only, practical class is in addition
The BWS is used in late mixed dentition, permanent dentition and in adults. Theory only, practical class is in addition
Which appliance to use and when it is most effective
Price for you $280 you save 40%
The required number of training hours 60 until 10/30/2022
4 lessons for 90 minutes each
How Cranial growth influences the shape, the size and the position of the jaws. The influence of cranial growth on the position of the teeth.
Prior to commencement of orthodontic or orthopaedic treatment, you must screen patients for myofunctional and airway issues.
Identifying Soft Tissue Dysfunction. The face is like a book, you need to learn how to read it.
Treating mild malocclusion in the early mixed dentition Using MRC Appliances
Price for you $280 you save 40%
The required number of training hours 60 until 10/30/2022
Cost of all 6 courses for participants is A$967 for you when buying all 6 courses. Full price $2280
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Yoga is a group of physical,mental and spiritual practices or disciplines which originated in ancient India. * For Children * For Young * For Old
Meditation is that which gives you deep rest.Meditation is an activityin which the practitioner just sits and allows the mind to dissolve.
Diet is the sum of food consumed by a person or other organism. The word diet often implies the use of specific intake of nutrition for health.


pywekaclassifiers arff documentation¶
-
class
pywekaclassifiers.arff.
ArffFile
(relation='', schema=None)[source]¶ An ARFF File object describes a data set consisting of a number of data points made up of attributes. The whole data set is called a ‘relation’. Supported attributes are:
- ‘numeric’: floating point numbers
- ‘string’: strings
- ‘nominal’: taking one of a number of possible values
Not all features of ARFF files are supported yet. The most notable exceptions are:
- no sparse data
- no support for date and relational attributes
Also, parsing of strings might still be a bit brittle.
You can either load or save from files, or write and parse from a string.
You can also construct an empty ARFF file and then fill in your data by hand. To define attributes use the define_attribute method.
Attributes are:
- ‘relation’: name of the relation
- ‘attributes’: names of the attributes
- ‘attribute_types’: types of the attributes
- ‘attribute_data’: additional data, for example for nominal attributes.
- ‘comment’: the initial comment in the file. Typically contains some
- information on the data set.
- ‘data’: the actual data, by data points.
-
alphabetize_attributes
()[source]¶ Orders attributes names alphabetically, except for the class attribute, which is kept last.
-
close_stream
()[source]¶ Terminates an open stream and returns the filename of the file containing the streamed data.
-
copy
(schema_only=False)[source]¶ Creates a deepcopy of the instance. If schema_only is True, the data will be excluded from the copy.
-
define_attribute
(name, atype, data=None)[source]¶ Define a new attribute. atype has to be one of ‘integer’, ‘real’, ‘numeric’, ‘string’, ‘date’ or ‘nominal’. For nominal attributes, pass the possible values as data. For date attributes, pass the format as data.
-
get_attribute_value
(name, index)[source]¶ Returns the value associated with the given value index of the attribute with the given name.
This is only applicable for nominal and string types.
-
open_stream
(class_attr_name=None, fn=None)[source]¶ Save an arff structure to a file, leaving the file object open for writing of new data samples. This prevents you from directly accessing the data via Python, but when generating a huge file, this prevents all your data from being stored in memory.
-
class
pywekaclassifiers.arff.
Value
(v, cls=False)[source]¶ Base helper class for tagging units of data with an explicit schema type.
-
cls
¶
-
value
¶
-


My name is Pratiksha Pandit. I'm a Geologist & Choreographer.
My education has provided me with a variety of information systems knowledge and practical skills; primarily in field of geology. I have Diploma Degree in Kathak & Bharatnatyam.
My work experience of working as Dance Instructor in Kendriya Vidhyalaya, Vadodara, India provided me good
command over teaching dance to students of different age group from beginner level to expert level.
I have experience in teaching dance forms like Kathak, Bharatnatyam, Bollywood and FreeStyle.


Sometimes Rarely, I pen down my musings in my blog. You can find my blog here.
Check out this YouTube video I made with Kush Grover to introduce Model Checking to school kids:


Halloween
Halloween or Hallowe'en (a contraction of Hallows' Even or Hallows' Evening),also known as Allhalloween,All
Hallows' Eve, or All Saints' Eve, is a celebration observed in many countries on 31 October, the eve of the
Western Christian feast of All Hallows' Day. It begins the three-day observance of Allhallowtide, the time
in the
liturgical year dedicated to remembering the dead, including saints (hallows), martyrs, and all the faithful
departed.
Halloween activities include trick-or-treating (or the related guising and souling), attending Halloween
costume
parties, carving pumpkins into jack-o'-lanterns, lighting bonfires, apple bobbing, divination games, playing
pranks,
visiting haunted attractions, telling scary stories, as well as watching horror films
Halloween activities include trick-or-treating (or the related guising and souling), attending Halloween
costume
parties, carving pumpkins into jack-o'-lanterns, lighting bonfires, apple bobbing, divination games, playing
pranks,
visiting haunted attractions, telling scary stories, as well as watching horror films
Haunted attractions are entertainment venues designed to thrill and scare patrons. Most attractions are
seasonal
Halloween businesses that may include haunted houses, corn mazes, and hayrides, and the level of
sophistication of
the effects has risen as the industry has grown.It was during the 1930s, about the same time as
trick-or-treating, that Halloween-themed haunted houses first began to
appear in America. It was in the late 1950s that haunted houses as a major attraction began to appear,
focusing first on
California. Sponsored by the Children's Health Home Junior Auxiliary, the San Mateo Haunted House opened in
1957. The
San Bernardino Assistance League Haunted House opened in 1958. Home haunts began appearing across the
country during
1962 and 1963. In 1964, the San Manteo Haunted House opened, as well as the Children's Museum Haunted House
in
Indianapolis.


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Basics of Machine Learning¶
Here is a “short” list of the most popular and up-to-date resources for learning machine learning and AI related topics.
“You should be practicing!”
Basic Courses¶
Stanford CS229 - Machine Learning
By Andrew Ng. This course provides a broad introduction to machine learning and statistical pattern recognition. Videos and lectures are available freely.
Cornell CS4780/CS5780 - Machine Learning for Intelligent Systems
Lectures and video recordings are available for free.
Dive into Deep Learning: an interactive deep learning book with code, math, and discussions
Handy Cheatsheets¶
Curated and Awesome List of Resources¶
-
A curated list of awesome machine learning frameworks, libraries and software (by language).
-
A curated list of awesome Deep Learning tutorials, projects and communities
-
A curated list of dedicated resources
Materials In Chinese¶
PumpkinBook - 《机器学习》（西瓜书）公式推导解析
In Chinese. Read online here
DeepLearning-500-questions - 深度学习500问
Deep learning through 500 questions, a Markdown book.
Tutorial and Examples¶
-
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning
-
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
-
A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks


To strategically increase the eCommerce activities and web presence of local SMEs and subsequently create free use of internet for simple SMEs in PNG.
To improve SME business activities in and around home. Creating newer opportunities with the use of IT solutions for Papua New Guineans.
Design your image Brand and conceptualize your theme.
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24/7 Server Support. Microsoft, Linux & Unix.
Smarter, cheaper and faster rate on the internet.
Traditional Advertising & Marketing with internet marketing concepts.
We'll sell, promote and make your product to your target customers.


Some quick example text to build on the card title and make up the bulk of the card's content.
Lorem ipsum dolor, sit amet consectetur adipisicing elit.
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Lorem ipsum dolor, sit amet consectetur adipisicing elit.
Some quick example text to build on the card title and make up the bulk of the card's content.
Lorem ipsum dolor, sit amet consectetur adipisicing elit.
Some quick example text to build on the card title and make up the bulk of the card's content.
Lorem ipsum dolor, sit amet consectetur adipisicing elit.
Some quick example text to build on the card title and make up the bulk of the card's content.
Lorem ipsum dolor, sit amet consectetur adipisicing elit.
Some quick example text to build on the card title and make up the bulk of the card's content.
Lorem ipsum dolor, sit amet consectetur adipisicing elit.
Some quick example text to build on the card title and make up the bulk of the card's content.
Lorem ipsum dolor, sit amet consectetur adipisicing elit.
Some quick example text to build on the card title and make up the bulk of the card's content.
U.S.
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Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
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World
National Popcorn Day Deals from Doc Popcorn, Showcase Cinemas and More
National Popcorn Day Deals from Doc Popcorn, Showcase Cinemas and More
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Bussiness
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National Popcorn Day Deals from Doc Popcorn, Showcase Cinemas and More
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Tech & Science
National Popcorn Day Deals from Doc Popcorn, Showcase Cinemas and More
National Popcorn Day Deals from Doc Popcorn, Showcase Cinemas and More
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Culture
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National Popcorn Day Deals from Doc Popcorn, Showcase Cinemas and More
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Top Democrat Blasts Rules Which Could Ease Oversight of Firearms Exports
Sports
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AFC Championship Game Preview: Can Andy Reid Reach a Super Bowl with KC?
AFC Championship Game Preview: Can Andy Reid Reach a Super Bowl with KC?
AFC Championship Game Preview: Can Andy Reid Reach a Super Bowl with KC?


| Interface | Description |
|---|---|
| BCPGKey |
base interface for a PGP key
|
| CompressionAlgorithmTags |
Basic tags for compression algorithms
|
| HashAlgorithmTags |
basic tags for hash algorithms
|
| PacketTags |
Basic PGP packet tag types.
|
| PublicKeyAlgorithmTags |
Public Key Algorithm tag numbers
|
| SignatureSubpacketTags |
Basic PGP signature sub-packet tag types.
|
| SymmetricKeyAlgorithmTags |
Basic tags for symmetric key algorithms
|
| UserAttributeSubpacketTags |
Basic PGP user attribute sub-packet tag types.
|
| Class | Description |
|---|---|
| ArmoredInputStream |
reader for Base64 armored objects - read the headers and then start returning
bytes when the data is reached.
|
| ArmoredOutputStream |
Output stream that writes data in ASCII Armored format.
|
| BCPGInputStream |
Stream reader for PGP objects
|
| BCPGObject |
Base class for a PGP object.
|
| BCPGOutputStream |
Basic output stream.
|
| CompressedDataPacket |
A generic compressed data object.
|
| ContainedPacket |
Basic type for a PGP packet.
|
| CRC24 | |
| DSAPublicBCPGKey |
base class for a DSA Public Key.
|
| DSASecretBCPGKey |
base class for a DSA Secret Key.
|
| ECDHPublicBCPGKey |
base class for an ECDH Public Key.
|
| ECDSAPublicBCPGKey |
base class for an ECDSA Public Key.
|
| ECPublicBCPGKey |
base class for an EC Public Key.
|
| ECSecretBCPGKey |
base class for an EC Secret Key.
|
| EdDSAPublicBCPGKey |
base class for an EdDSA Public Key.
|
| EdSecretBCPGKey |
base class for an Edwards Curve Secret Key.
|
| ElGamalPublicBCPGKey |
base class for an ElGamal Public Key.
|
| ElGamalSecretBCPGKey |
base class for an ElGamal Secret Key.
|
| ExperimentalPacket |
basic packet for an experimental packet.
|
| InputStreamPacket |
A block of data associated with other packets in a PGP object stream.
|
| LiteralDataPacket |
Generic literal data packet.
|
| MarkerPacket |
Basic type for a marker packet
|
| ModDetectionCodePacket |
basic packet for a modification detection code packet.
|
| MPInteger |
a multiple precision integer
|
| OnePassSignaturePacket |
generic signature object
|
| OutputStreamPacket | |
| Packet | |
| PublicKeyEncSessionPacket |
basic packet for a PGP public key
|
| PublicKeyPacket |
basic packet for a PGP public key
|
| PublicSubkeyPacket |
basic packet for a PGP public key
|
| RSAPublicBCPGKey |
base class for an RSA Public Key.
|
| RSASecretBCPGKey |
base class for an RSA Secret (or Private) Key.
|
| S2K |
Parameter specifier for the PGP string-to-key password based key derivation function.
|
| SecretKeyPacket |
basic packet for a PGP secret key
|
| SecretSubkeyPacket |
basic packet for a PGP secret key
|
| SignaturePacket |
generic signature packet
|
| SignatureSubpacket |
Basic type for a PGP Signature sub-packet.
|
| SignatureSubpacketInputStream |
reader for signature sub-packets
|
| SymmetricEncDataPacket |
Basic type for a symmetric key encrypted packet
|
| SymmetricEncIntegrityPacket |
A symmetric key encrypted packet with an associated integrity check code.
|
| SymmetricKeyEncSessionPacket |
Basic type for a symmetric encrypted session key packet
|
| TrustPacket |
Basic type for a trust packet
|
| UserAttributePacket |
Basic type for a user attribute packet.
|
| UserAttributeSubpacket |
Basic type for a user attribute sub-packet.
|
| UserAttributeSubpacketInputStream |
reader for user attribute sub-packets
|
| UserIDPacket |
Basic type for a user ID packet.
|
These classes deal with things at a raw OpenPGP packet level. For the most part you are probably better off looking at the org.bouncycastle.openpgp package for what you want.


|
yaml
0.1.6
|
The emitter structure. More...
#include <yaml.h>
Data Fields | |
| unsigned char * | buffer |
| The buffer pointer. | |
| size_t | size |
| The buffer size. | |
| size_t * | size_written |
| The number of written bytes. | |
| struct { | |
| unsigned char * buffer | |
| The buffer pointer. | |
| size_t size | |
| The buffer size. | |
| size_t * size_written | |
| The number of written bytes. | |
| } | string |
| String output data. | |
| FILE * | file |
| File output data. | |
| yaml_char_t * | start |
| The beginning of the buffer. | |
| yaml_char_t * | end |
| The end of the buffer. | |
| yaml_char_t * | pointer |
| The current position of the buffer. | |
| yaml_char_t * | last |
| The last filled position of the buffer. | |
| unsigned char * | start |
| The beginning of the buffer. | |
| unsigned char * | end |
| The end of the buffer. | |
| unsigned char * | pointer |
| The current position of the buffer. | |
| unsigned char * | last |
| The last filled position of the buffer. | |
| yaml_emitter_state_t * | start |
| The beginning of the stack. | |
| yaml_emitter_state_t * | end |
| The end of the stack. | |
| yaml_emitter_state_t * | top |
| The top of the stack. | |
| yaml_event_t * | start |
| The beginning of the event queue. | |
| yaml_event_t * | end |
| The end of the event queue. | |
| yaml_event_t * | head |
| The head of the event queue. | |
| yaml_event_t * | tail |
| The tail of the event queue. | |
| int * | start |
| The beginning of the stack. | |
| int * | end |
| The end of the stack. | |
| int * | top |
| The top of the stack. | |
| yaml_tag_directive_t * | start |
| The beginning of the list. | |
| yaml_tag_directive_t * | end |
| The end of the list. | |
| yaml_tag_directive_t * | top |
| The top of the list. | |
| yaml_char_t * | anchor |
| The anchor value. | |
| size_t | anchor_length |
| The anchor length. | |
| int | alias |
| Is it an alias? | |
| yaml_char_t * | handle |
| The tag handle. | |
| size_t | handle_length |
| The tag handle length. | |
| yaml_char_t * | suffix |
| The tag suffix. | |
| size_t | suffix_length |
| The tag suffix length. | |
| yaml_char_t * | value |
| The scalar value. | |
| size_t | length |
| The scalar length. | |
| int | multiline |
| Does the scalar contain line breaks? | |
| int | flow_plain_allowed |
| Can the scalar be expessed in the flow plain style? | |
| int | block_plain_allowed |
| Can the scalar be expressed in the block plain style? | |
| int | single_quoted_allowed |
| Can the scalar be expressed in the single quoted style? | |
| int | block_allowed |
| Can the scalar be expressed in the literal or folded styles? | |
| yaml_scalar_style_t | style |
| The output style. | |
| int | references |
| The number of references. | |
| int | anchor |
| The anchor id. | |
| int | serialized |
| If the node has been emitted? | |
Error handling | |
| yaml_error_type_t | error |
| Error type. | |
| const char * | problem |
| Error description. | |
Writer stuff | |
| yaml_write_handler_t * | write_handler |
| Write handler. | |
| void * | write_handler_data |
| A pointer for passing to the white handler. | |
| union { | |
| struct { | |
| unsigned char * buffer | |
| The buffer pointer. | |
| size_t size | |
| The buffer size. | |
| size_t * size_written | |
| The number of written bytes. | |
| } string | |
| String output data. | |
| FILE * file | |
| File output data. | |
| } | output |
| Standard (string or file) output data. | |
| struct { | |
| yaml_char_t * start | |
| The beginning of the buffer. | |
| yaml_char_t * end | |
| The end of the buffer. | |
| yaml_char_t * pointer | |
| The current position of the buffer. | |
| yaml_char_t * last | |
| The last filled position of the buffer. | |
| } | buffer |
| The working buffer. | |
| struct { | |
| unsigned char * start | |
| The beginning of the buffer. | |
| unsigned char * end | |
| The end of the buffer. | |
| unsigned char * pointer | |
| The current position of the buffer. | |
| unsigned char * last | |
| The last filled position of the buffer. | |
| } | raw_buffer |
| The raw buffer. | |
| yaml_encoding_t | encoding |
| The stream encoding. | |
Emitter stuff | |
| int | canonical |
| If the output is in the canonical style? | |
| int | best_indent |
| The number of indentation spaces. | |
| int | best_width |
| The preferred width of the output lines. | |
| int | unicode |
| Allow unescaped non-ASCII characters? | |
| yaml_break_t | line_break |
| The preferred line break. | |
| struct { | |
| yaml_emitter_state_t * start | |
| The beginning of the stack. | |
| yaml_emitter_state_t * end | |
| The end of the stack. | |
| yaml_emitter_state_t * top | |
| The top of the stack. | |
| } | states |
| The stack of states. | |
| yaml_emitter_state_t | state |
| The current emitter state. | |
| struct { | |
| yaml_event_t * start | |
| The beginning of the event queue. | |
| yaml_event_t * end | |
| The end of the event queue. | |
| yaml_event_t * head | |
| The head of the event queue. | |
| yaml_event_t * tail | |
| The tail of the event queue. | |
| } | events |
| The event queue. | |
| struct { | |
| int * start | |
| The beginning of the stack. | |
| int * end | |
| The end of the stack. | |
| int * top | |
| The top of the stack. | |
| } | indents |
| The stack of indentation levels. | |
| struct { | |
| yaml_tag_directive_t * start | |
| The beginning of the list. | |
| yaml_tag_directive_t * end | |
| The end of the list. | |
| yaml_tag_directive_t * top | |
| The top of the list. | |
| } | tag_directives |
| The list of tag directives. | |
| int | indent |
| The current indentation level. | |
| int | flow_level |
| The current flow level. | |
| int | root_context |
| Is it the document root context? | |
| int | sequence_context |
| Is it a sequence context? | |
| int | mapping_context |
| Is it a mapping context? | |
| int | simple_key_context |
| Is it a simple mapping key context? | |
| int | line |
| The current line. | |
| int | column |
| The current column. | |
| int | whitespace |
| If the last character was a whitespace? | |
| int | indention |
| If the last character was an indentation character (' ', '-', '?', ':')? | |
| int | open_ended |
| If an explicit document end is required? | |
| struct { | |
| yaml_char_t * anchor | |
| The anchor value. | |
| size_t anchor_length | |
| The anchor length. | |
| int alias | |
| Is it an alias? | |
| } | anchor_data |
| Anchor analysis. | |
| struct { | |
| yaml_char_t * handle | |
| The tag handle. | |
| size_t handle_length | |
| The tag handle length. | |
| yaml_char_t * suffix | |
| The tag suffix. | |
| size_t suffix_length | |
| The tag suffix length. | |
| } | tag_data |
| Tag analysis. | |
| struct { | |
| yaml_char_t * value | |
| The scalar value. | |
| size_t length | |
| The scalar length. | |
| int multiline | |
| Does the scalar contain line breaks? | |
| int flow_plain_allowed | |
| Can the scalar be expessed in the flow plain style? | |
| int block_plain_allowed | |
| Can the scalar be expressed in the block plain style? | |
| int single_quoted_allowed | |
| Can the scalar be expressed in the single quoted style? | |
| int block_allowed | |
| Can the scalar be expressed in the literal or folded styles? | |
| yaml_scalar_style_t style | |
| The output style. | |
| } | scalar_data |
| Scalar analysis. | |
Dumper stuff | |
| int | opened |
| If the stream was already opened? | |
| int | closed |
| If the stream was already closed? | |
| struct { | |
| int references | |
| The number of references. | |
| int anchor | |
| The anchor id. | |
| int serialized | |
| If the node has been emitted? | |
| } | anchors |
| The information associated with the document nodes. | |
| int | last_anchor_id |
| The last assigned anchor id. | |
| yaml_document_t * | document |
| The currently emitted document. |
The emitter structure.
All members are internal. Manage the structure using the yaml_emitter_
family of functions.
Error type.
| const char* yaml_emitter_s::problem |
Error description.
Write handler.
A pointer for passing to the white handler.
| unsigned char* yaml_emitter_s::buffer |
The buffer pointer.
| size_t yaml_emitter_s::size |
The buffer size.
| size_t* yaml_emitter_s::size_written |
The number of written bytes.
| struct { ... } yaml_emitter_s::string |
String output data.
| FILE* yaml_emitter_s::file |
File output data.
| union { ... } yaml_emitter_s::output |
Standard (string or file) output data.
The beginning of the buffer.
The end of the buffer.
The current position of the buffer.
The last filled position of the buffer.
| struct { ... } yaml_emitter_s::buffer |
The working buffer.
| unsigned char* yaml_emitter_s::start |
The beginning of the buffer.
| unsigned char* yaml_emitter_s::end |
The end of the buffer.
| unsigned char* yaml_emitter_s::pointer |
The current position of the buffer.
| unsigned char* yaml_emitter_s::last |
The last filled position of the buffer.
| struct { ... } yaml_emitter_s::raw_buffer |
The raw buffer.
The stream encoding.
The number of indentation spaces.
The preferred width of the output lines.
The preferred line break.
The beginning of the stack.
The end of the stack.
The top of the stack.
| struct { ... } yaml_emitter_s::states |
The stack of states.
The current emitter state.
The beginning of the event queue.
The end of the event queue.
The head of the event queue.
The tail of the event queue.
| struct { ... } yaml_emitter_s::events |
The event queue.
The beginning of the stack.
| int* yaml_emitter_s::end |
The end of the stack.
| int* yaml_emitter_s::top |
The top of the stack.
| struct { ... } yaml_emitter_s::indents |
The stack of indentation levels.
The beginning of the list.
The end of the list.
The top of the list.
| struct { ... } yaml_emitter_s::tag_directives |
The list of tag directives.
The current indentation level.
The current flow level.
The current line.
The current column.
The anchor value.
The anchor length.
| struct { ... } yaml_emitter_s::anchor_data |
Anchor analysis.
The tag handle.
The tag handle length.
The tag suffix.
The tag suffix length.
| struct { ... } yaml_emitter_s::tag_data |
Tag analysis.
The scalar value.
| size_t yaml_emitter_s::length |
The scalar length.
The output style.
| struct { ... } yaml_emitter_s::scalar_data |
Scalar analysis.
The number of references.
The anchor id.
| struct { ... } * yaml_emitter_s::anchors |
The information associated with the document nodes.
The last assigned anchor id.
The currently emitted document.


The
utility provides virtual half-line and reverse line feed sequences for terminals without such capability, and on which overstriking is destructive. Half-line characters and underlining (changed to dashing `-') are placed on new lines in between the normal output lines.
The following options are available:
Suppress all underlining. This option is especially useful for previewing
tables from
Cause all half-lines to be printed, effectively double spacing the output. Normally, a minimal space output format is used which will suppress empty lines. The program never suppresses two consecutive empty lines, however. The
option is useful for sending output to the line printer when the output contains superscripts and subscripts which would otherwise be invisible.
The
and
environment variables affect the execution of
as described in
A typical use of
would be
tbl exum2.n | nroff -ms | colcrt - | more
The
command appeared in
Should fold underlines onto blanks even with the
option so that a true underline character would show.
Cannot back up more than 102 lines.
General overstriking is lost; as a special case
overstruck with
or underline becomes
Lines are trimmed to 132 characters.
Some provision should be made for processing superscripts and subscripts in documents which are already double-spaced.
Characters that take up more than one column position may not be underlined correctly.


Angular is pure client-side technology, written entirely in JavaScript. It works with the long-established technologies of the web (HTML, CSS, and JavaScript) to make the development of web apps easier and faster than ever before.
One important way that Angular simplifies web development is by increasing the level of abstraction between the developer and most low-level web app development tasks. Angular automatically takes care of many of these tasks, including:
Because Angular handles much of the work involved in these tasks, developers can concentrate more on application logic and less on repetitive, error-prone, lower-level coding.
At the same time that Angular simplifies the development of web apps, it brings relatively sophisticated techniques to the client-side, including:
These techniques have been for the most part absent from the client-side for far too long.
You can use Angular to develop both single-page and round-trip apps, but Angular is designed primarily for developing single-page apps. Angular supports browser history, forward and back buttons, and bookmarking in single-page apps.
You normally wouldn't want to load Angular with every page change, as would be the case with using Angular in a round-trip app. However, it would make sense to do so if you were adding a subset of Angular's features (for example, templates to leverage angular's data-binding feature) to an existing round-trip app. You might follow this course of action if you were migrating an older app to a single-page Angular app.


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Look what it can do.
Look inside objects
Look at effects applied on photos through mag glass
You can make instant retouch if you wish


This interesting, attractive town was, in the past, a busy whaling station and is one of the oldest cities in Brazil.
Position 26°14'S, 48°38'W.
This is a port of entry.
Read Brazil Formalities for full details on clearing into and out of Brazil.
It is possible to anchor in front of the jetty on the waterfront and also off the jetty by the museum in the downtown area.
Further into the bay, there are pontoons at the yacht club at Joinville and one at the Iate Clube de Capri. The channel to which is quite shallow and approach need to be made above half tide in most yachts.
Last updated January 2010.


Now it hardly looks like robberies are changing at all. They might have gone up slightly, but it is hardly a massive crime wave happening in 2016 that was not happening in 2014. With the graph in Figure 3.6.5, would you want to devote a lot of public resources to stemming the rising tide of robberies? No one wants to be robbed, but it is not exactly a terrifying increase.
in-context


“The German mind was then at the height of its emotional unrestraint… and it produced a new sect or religious distinction almost every day. Many of these sects came to Pennsylvania, where new small religious bodies sprang up among them after their arrival. Schwenkfelders, Tunkers, Labadists, New Born, New Mooners, Separatists, Zion's Brueder, Ronsdorfer, Inspired, Quietists, Gichtelians, Depellians, Mountain Men, River Brethren, Brinser Brethren, and the Society of the Woman in the Wilderness, are names which occur in the annals of the province.” — Sydney G Fisher, The Quaker Colonies


--- title: Batch Norm Folding keywords: fastai sidebar: home_sidebar summary: "Fold the batchnorm and the conv layers together to reduce computation" description: "Fold the batchnorm and the conv layers together to reduce computation" nb_path: "nbs/06_bn_folding.ipynb" ---
Batch Normalization is a technique which takes care of normalizing the input of each layer to make the training process faster and more stable. In practice, it is an extra layer that we generally add after the computation layer and before the non-linearity.
It consists of 2 steps:
Due to its efficiency for training neural networks, batch normalization is now widely used. But how useful is it at inference time?
Once the training has ended, each batch normalization layer possesses a specific set of $\gamma$ and $\beta$, but also $\mu$ and $\sigma$, the latter being computed using an exponentially weighted average during training. It means that during inference, the batch normalization acts as a simple linear transformation of what comes out of the previous layer, often a convolution.
As a convolution is also a linear transformation, it also means that both operations can be merged into a single linear transformation!
This would remove some unnecessary parameters but also reduce the number of operations to be performed at inference time.
With a little bit of math, we can easily rearrange the terms of the convolution to take the batch normalization into account.
As a little reminder, the convolution operation followed by the batch normalization operation can be expressed, for an input $x$, as:
{% raw %} $$\begin{aligned} z &=W * x+b \\ \mathrm{out} &=\gamma \cdot \frac{z-\mu}{\sqrt{\sigma^{2}+\epsilon}}+\beta \end{aligned}$$ {% endraw %}
So, if we re-arrange the $W$ and $b$ of the convolution to take the parameters of the batch normalization into account, as such:
{% raw %} $$\begin{aligned} w_{\text {fold }} &=\gamma \cdot \frac{W}{\sqrt{\sigma^{2}+\epsilon}} \\ b_{\text {fold }} &=\gamma \cdot \frac{b-\mu}{\sqrt{\sigma^{2}+\epsilon}}+\beta \end{aligned}$$ {% endraw %}
This is how to do it with fasterai !
path = untar_data(URLs.PETS)
files = get_image_files(path/"images")
def label_func(f): return f[0].isupper()
dls = ImageDataLoaders.from_name_func(path, files, label_func, item_tfms=Resize(64))
learn = Learner(dls, resnet18(num_classes=2), metrics=accuracy)
learn.fit_one_cycle(5)
bn = BN_Folder()
new_model = bn.fold(learn.model)
The batch norm layers have been replaced by an Identity layer, and the weights of the convolutions have been modified accordingly.
new_model
We can see that the new model possess fewer parameters
count_parameters(learn.model)
count_parameters(new_model)
But is also faster to run !
x,y = dls.one_batch()
%%timeit
learn.model(x[0][None].cuda())
%%timeit
new_model(x[0][None].cuda())
But most importantly, has the exact same perfomance as before:
new_learn = Learner(dls, new_model, metrics=accuracy)
new_learn.validate()


Changelog
MiniSearch
follows semantic versioning.
v2.2.2
Avoid destructuring variables named with an underscore prefix. This plays nicer to some common minifier and builder configurations.
Performance improvement in
getDefault
(by stalniy)Fix the linter setup, to ensure code style consistency
v2.2.1
- Add
"sideEffects": false
topackage.json
to allow bundlers to perform tree shaking
v2.2.0
- [fix] Fix documentation of
SearchableMap.prototype.atPrefix
(by @graphman65) - Switch to Rollup for bundling (by stalniy), reducing size of build and providing ES6 and ES5 module versions too.
v2.1.4
- [fix] Fix document removal in presence of custom per field tokenizer, field extractor, or term processor (thanks @CaptainChaos)
v2.1.3
- [fix] Fix TypeScript definition for
storeFields
option (by @ryan-codingintrigue)
v2.1.2
- [fix] Fix TypeScript definition for
fuzzy
option (by @alessandrobardini)
v2.1.1
- [fix] Fix TypeScript definitions adding
filter
andstoreFields
options (by @emilianox)
v2.1.0
[feature] Add support for stored fields
[feature] Add filtering of search results and auto suggestions
v2.0.6
- Better TypeScript definitions (by @samuelmeuli)
v2.0.5
- Add TypeScript definitions for ease of use in TypeScript projects
v2.0.4
- [fix] tokenizer behavior with newline characters (by @samuelmeuli)
v2.0.3
- Fix small imprecision in documentation
v2.0.2
- Add
addAllAsync
method, adding many documents asynchronously and in chunks to avoid blocking the main thread
v2.0.1
- Throw a more descriptive error when
loadJSON
is called without options
v2.0.0
This release introduces better defaults. It is considered a major release, as the default options are slightly different, but the API is not changed.
Breaking change: default tokenizer splits by Unicode space or punctuation (before it was splitting by space, punctuation, or symbol). The difference is that currency symbols and other non-punctuation symbols will not be discarded: "it's 100€" is now tokenized as
["it", "s", "100€"]
instead of["it", "s", "100"]
.Breaking change: default term processing does not discard 1-character words.
Breaking change: auto suggestions by default perform prefix search only on the last term in the query. So "super cond" will suggest "super conductivity", but not "superposition condition".
v1.3.1
- Better and more compact regular expression in the default tokenizer, separating on Unicode spaces, punctuation, and symbols
v1.3.0
- Support for non-latin scripts
v1.2.1
- Improve fuzzy search performance (common cases are now ~4x faster, as shown by the benchmark)
v1.2.0
- Add possibility to configure a custom field extraction function by setting
the
extractField
option (to support cases like nested fields, non-string fields, getter methods, field pre-processing, etc.)
v1.1.2
- Add
getDefault
static method to get the default value of configuration options
v1.1.1
- Do not minify library when published as NPM package. Run
yarn build-minified
(ornpm run build-minified
) to produce a minified build with source maps. - Bugfix: as per specification,
processTerm
is called with only one argument upon search (see #5)
v1.1.0
- Add possibility to configure separate index-time and search-time tokenization and term processing functions
- The
processTerm
function can now reject a term by returning a falsy value - Upon indexing, the
tokenize
andprocessTerm
functions receive the field name as the second argument. This makes it possible to process or tokenize each field differently.
v1.0.1
- Reduce bundle size by optimizing babel preset env options
v1.0.0
Production-ready release.
Features:
- Space-optimized index
- Exact match, prefix match, fuzzy search
- Auto suggestions
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To make your busnisse is more awesome you should do many thing , like effictive mange of your busnisse, hire good persons, identify goals and tasks, and mange finicial topics in busnisse in right way.


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canPlay
audioObject.currentTime:The current time of the object in seconds
audioObject.progress:The progress of the object as a percentage (0-1);


--- layout: post status: publish published: true title: Flat Roller Coasters Suck - Episode 8.38 author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 11948 wordpress_url: http://ivyenvy.com/?p=11948 date: '2016-07-20 22:42:46 +0000' date_gmt: '2016-07-21 03:42:46 +0000' categories: - Podcast tags: [] comments: - id: 24330 author: Matthew Boyd author_email: '' author_url: '' date: '2016-07-22 03:07:48 +0000' date_gmt: '2016-07-22 08:07:48 +0000' content: "Everything you said about Aroldis Chapman in this podcast is every reason I do not want the Cubs to pursue that guy, and I appreciate your putting your feelings on the line when a lot of the jersey-buying/internet-flexing crowd don't really care. \nI do not want that guy pumping his fist after closing out the World Series on the cover of SI. \nI want our front office to build a team that doesn't need that kind of a sociopath to win a title. I remember Limited Range saying this and I remember Julie DiCaro talking about this...it matters to me, and it matters when you consider that the pleasure of winning should match the means that it took. \nIsn't that Joe's ethos? \nThe pleasure matching the pressure? \nI'd rather wait until 2017 than see Aroldis Chapman in a Cubs uniform. \nI'd honestly rather savor last season and see some progress this than sell our souls out to obtain that guy.\nSmart (brilliant, cruel) trades with the A's and the Rangers, good draft picks, the best scouts, a good team mentality, smart bullpen management, the right kind of potato chips...I'd rather wait until 2017 than see Aroldis Chapman in a Cubs uniform on the cover of Sports Illustrated." ---
Thanks for downloading the 416th episode of The Ivy Envy Podcast. On this episode, we talk about the Cubs series win over the New York Mets. The Cubs won the series 2 games to 1, but because Andy and Corey were at Tuesday night's loss, we spend some time talking about the Cubs difficulties with hitting with runners in scoring position. We also look at the Hector Rondon and discuss whether or not he is "the guy".
It's not all negatives though, Jake Arrieta looked like the Jake we all know and love. Oh yeah, and the Cubs traded for a pitcher and sent Dan Vogelbach to the Mariners. We talk about this trade and how it could impact the Cubs now and in the future.
Save 20% on Ivy Envy shirts at IvyEnvy.com/store by using the coupon code JAVY.


Calculator
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Creating a simple REST API
backend in Node.js using the Express.js framework.
GET /movies
Return the list with only id
and title
of all available movies.
GET /movies/:id
Return the full data of a specific movie.
POST /movies
Create a new entry in the database.
PUT /movies/:id
Update the full data of a specific movie.
DELETE /movies/:id
Remove an entry from the database.


→Lindsay is a creative and out of the box thinker and who uses problem solving to help create her designs. She enjoys working with clients and helping them to bring their visions to reality. Raised in the Bay Area, Lindsay helps local small businesses with logos, designs, and websites.
→AA in Arts and Humanities from Los Medanos College, Pittsburgh, CA 2013
→Internship for Graphic Design, Anchor Steam Brewery, San Francisco CA 2014-16
→BA in Grpahic Design from CSU East Bay, Hayward, CA 2018


{# Index page #} {% extends "core/core_base.html" %} {% load static %} {% block title %} Hello {% endblock %} {% block styles %} {% endblock %} {% block content %}
Open on your PC, TV or smartphone to become a Russian!
Save all cards which you really need and have a fast access to it!
They already recorded for you, just use them wherever you are!
Some people arrive in our country
without knowing the language itself.
However, they usually need a way to communicate
with Russian citizens for different purposes.
We created the dictionary from zero collecting the
most useful phrases and words.


DIFFICULTY LEVELS
IN MEDIAN XL: ULTIMATIVE
Just like regular Diablo II Lord of Destruction, Median XL features three difficulty levels. Killing Baal unlocks the next difficulty level for that character.
Hatred
Level range (regular game) : 1-55
Level range (uberquests) : 60-115
The first and easiest difficulty level. Regular monsters range from 50 to 10K health and deal 1 to 200 damage. Their AI is not very bright and leech works at 1/4 effectiveness, which should be just fine considering the increased amount of damage characters can deal compared to the regular game.
Terror
Level range (regular game) : 55-115
Level range (uberquests) : 115
A significant step up in difficulty, regular monsters range from 10K to 100K health and deal 250 to 750 damage. Leech is reduced to 1/50 effectiveness. There is a 50% resistances penalty. If you can beat Terror, you have completed the regular game. You can expect to hit the level cap of 120 in Terror difficulty.
Sets drop in all Uberquest areas.
Destruction
Level range (regular game) : 120
Level range (uberquests) : 120
After reaching the level cap, Inferno awaits. Every monster is level 120 and is extremely aggressive. Regular monsters range from 100K to 300K health and deal 500 to 1000 damage, but uberquest monsters go far beyond that. Abandon all hope. Leech is reduced to 1/100 to make up for high attack damage at this point in the game and there's a 100% resistances penalty.
Sacred uniques drop only in Destruction difficulty.


LETTER OF AUTHORIZATION
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below to Zerodraft Maryland, LLC. This data will be used for the confidential purpose of evaluating my usage
and load patterns.
Electric Utility: Please send all appropriate billing data to the fax number or email address listed
at bottom of LOA, including but not limited to:
• Rate Schedule, including any options
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o KVAR or KVA and power factor
• Capacity Tags
**PLEASE REVIEW YOUR INFORMATION IN PREVIOUS SECTIONS. YOUR INFORMATION WILL BE INSERTED HERE.**


this is the header
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stack module
Use a stack to check if brackets match correctly in a string.
- examples.stack.brackets_match(string)
Check if all brackets in the string are correctly matched.
Return True if they are, otherwise False. Only consider round and square brackets, i.e. () and []. For example:
>>> brackets_match("No brackets is fine") True >>> brackets_match("After ( and [ comes ] and )") True >>> brackets_match("After ( and [ it cannot be ) and ]") False >>> brackets_match("A ( without closing bracket") False >>> brackets_match("A ] without opening bracket") False >>> brackets_match("Snarks are rarely seen (according to Smith (1999)).") True


LEET SKETCH
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A
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Z
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]
N
N
(in
ChemElements
)
neqsteppropagator
(in
csb.statistics.samplers.mc
)
NotG
(in
NucleicAlphabet
)
n
(in
MaxentModel
)
new()
(in
GaussianMixture
)
NotIn
(in
csb.io.tsv
)
N
(in
GaussianMixture
)
newline
(in
EntryWriter
)
notin()
(in
Where
)
Na
(in
ChemElements
)
NEWLINE
(in
csb.io
)
NotT
(in
NucleicAlphabet
)
name
(in
ProfileHMM
)
next()
(in
A3MSequenceIterator
)
Np
(in
ChemElements
)
name
(in
ClansEntry
)
next()
(in
CompositeEntityIterator
)
nr
(in
HeaderInfo
)
name
(in
ClansSeqgroup
)
next()
(in
Looper
)
nrmsd_to()
(in
Assignment
)
name
(in
Atom
)
Ni
(in
ChemElements
)
nrstart
(in
HeaderInfo
)
name
(in
Chain
)
nmr
(in
csb.bio
)
ns
(in
HeaderInfo
)
name
(in
EnumItem
)
No
(in
ChemElements
)
nsstart
(in
HeaderInfo
)
name
(in
TempFile
)
no_of_seqs
(in
HHpredHitList
)
nsteps
(in
HMCSampler
)
name
(in
TempFolder
)
NO_OUTPUT
(in
ExitCodes
)
nsymbt
(in
HeaderInfo
)
name
(in
ColumnInfo
)
NO_OUTPUT
(in
ExitCodes
)
NUCLEI
(in
ChemShiftScoringModel
)
name
(in
Table
)
noe
(in
csb.bio.io
)
NucleicAcid
(in
SequenceTypes
)
name
(in
AbstractParameter
)
NOEPeak
(in
csb.bio.nmr
)
NucleicAlphabet
(in
csb.bio.sequence
)
NAMED
(in
Type
)
NOESpectrum
(in
csb.bio.nmr
)
NucleicResidue
(in
csb.bio.structure
)
names()
(in
Enum
)
NonequilibriumStepPropagator
(in
csb.statistics.samplers.mc.neqsteppropagator
)
NULL
(in
Table
)
namespace
(in
Console
)
NonequilibriumTrajectory
(in
csb.statistics.samplers.mc.neqsteppropagator
)
NULL
(in
AbstractParameter
)
native
(in
TargetResidue
)
NonparametricWHAM
(in
csb.statmech.wham
)
NullEstimator
(in
csb.statistics.pdf
)
NATIVE
(in
ByteOrder
)
NonVirtualParameter
(in
csb.statistics.pdf.parameterized
)
num_dimensions
(in
NOEPeak
)
Nb
(in
ChemElements
)
NoOutputError
(in
csb.apps.buildhmm
)
num_dimensions
(in
NOESpectrum
)
nc
(in
HeaderInfo
)
norm()
(in
csb.numeric
)
num_proton_dimensions
(in
NOESpectrum
)
NCMCProposalCommunicator
(in
csb.statistics.samplers.mc.singlechain
)
Normal
(in
csb.statistics.pdf
)
number
(in
Chart
)
ncstart
(in
HeaderInfo
)
normalize()
(in
MaxentModel
)
number
(in
DataRow
)
Nd
(in
ChemElements
)
NORMALIZED_RMSD
(in
Metrics
)
numeric
(in
csb
)
Ne
(in
ChemElements
)
NotA
(in
NucleicAlphabet
)
nx
(in
HeaderInfo
)
neff
(in
Assignment
)
NotC
(in
NucleicAlphabet
)
ny
(in
HeaderInfo
)
neff
(in
HHpredHitList
)
NotEquals
(in
csb.io.tsv
)
nz
(in
HeaderInfo
)
negative()
(in
ChemShiftScoringModel
)
notequals()
(in
Where
)
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