MAI-IML Exercise 4: Adaboost from Scratch and Predicting Customer Churn
Abstract
In this work, we develop a custom adaboost classifier compatible with the sklearn package and test it on a dataset from a telecommunication company requiring the correct classification of custumers likely to "churn", or quit their services, for use in developing investment plans to retain these high risk customers.
Adaboost from Scratch
Defined below is an sklearn compatable estimator utilizing the adaboost algorithm to perform binary classification. The input parameters for this estimator is the number of weak learners (which are decision tree stubs on a single, randomly selected feature) to train and aggregate to produce the final classifier. An optional weight distribution can also be passed to the classifier, which defaults to uniform if not set. This custom estimator will later be utilized to develop a classifier capable of predicting customer churn from labelled customer data.
#imports
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas.tools.plotting import scatter_matrix
import seaborn as sns
from sklearn.feature_selection import SelectKBest
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
#Custom Classifier
from sklearn.utils.estimator_checks import check_estimator
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
class IMLAdaBoostClassifier(BaseEstimator, ClassifierMixin):
""" IML implementation of an Adaboost Classifier.
An implementation of the Adaboost algorithm for classification using decision stubs as the weak learner with n_estimators
parameter. Weights are initalized as uniform, 1/N and learning rate alpha is calculated using 1/2ln(1-err/err) at each
estimator timestep.
Parameters
----------
n_estimators : integer, optional (default=50)
The maximum number of estimators at which boosting is terminated.
weights : list, optional (default=uniform)
The distribution of weights for each example
Attributes
----------
weak_learners : 3-tuple of weak_learner stub definition of - [feature stub column index, stub split value, greater then or less
then equality tag]
classes_ : array of shape = [n_classes]
The classes labels.
alphas : array of floats
Weights for each estimator in the boosted ensemble.
"""
def __init__(self, n_estimators=50,weights=[]):
self.n_estimators = n_estimators
self.weak_learners = []
self.alphas = []
self.weights = weights
def fit(self, X, y):
# Check that X and y have correct shape
X, y = check_X_y(X, y)
# Store the classes seen during fit
self.classes_ = unique_labels(y)
#initialize wents with uniform distribution
if self.weights == []:
weights = [1/len(y) for lbl in y]
else:
weights = self.weights
for n in range(self.n_estimators):
#for each n estimator, build and store the weak learner with associated alpha's
weak_learner, alpha, weights = self.decision_stump(X,y,weights)
if weak_learner:
self.weak_learners.append(weak_learner)
self.alphas.append(alpha)
self.alphas = [tmp_a/sum(self.alphas) for tmp_a in self.alphas]#normalize final alphas
self.X_ = X
self.y_ = y
# Return the classifier
return self
def predict(self, X):
# Check is fit had been called
check_is_fitted(self, ['X_', 'y_'])
# Input validation
X = check_array(X)
#for each predicting sample, aggregate the weak_learnings by weighted sum and return the sign of the result as the pred
df_preds = self.decision_function(X)
preds = np.array([np.sign(tmp_pred) for tmp_pred in df_preds])
return preds
def decision_function(self, X):
preds = []
for smpl in X:
tmp_pred = 0
for i,estimator in enumerate(self.weak_learners):
#get feature and split value from 3-tuple weak_learner [split_feature_indx,split_value, 'gt' or 'lt' tag]
split_feature = estimator[0]
split_value = estimator[1]
#check the weak_learner inequality rule for the smpl and increment aggregation function
if estimator[2] == 'gt':
if smpl[split_feature] >= split_value:
tmp_pred = tmp_pred + self.alphas[i]*1
else:
tmp_pred = tmp_pred + self.alphas[i]*-1
elif estimator[2] == 'lt':
if smpl[split_feature] <= split_value:
tmp_pred = tmp_pred + self.alphas[i]*1
else:
tmp_pred = tmp_pred + self.alphas[i]*-1
preds.append(tmp_pred)
return np.array(preds)
def decision_stump(self,X,y,weights):
#create a weak_learning by creating a decision stump by selecting a random feature to split on, using the values
#in the feature as split values to test until a weak learner (< 50% error) is found for either the greater then or
#less then split value inequality test. return the created weak learning, updated weights and associated alpha
rnd_column_indx = np.random.randint(0,X.shape[1])
rnd_column = X[:,rnd_column_indx]
weak_learner = []
alpha = 0
unique_vals = list(set(rnd_column)) #create a shuffled list of unique values in selected column to test split values from
np.random.shuffle(unique_vals)
for split_val in unique_vals:
#check the error rates for stub with split value for < or > rule, if err <50% return resulting weak_learner, alpha
#and new weights
gt_prd_labels = [1 if check_val >= split_val else -1 for check_val in rnd_column]
#add small error amount to prevent div by 0 error
gt_err = sum([0 if y[i]*gt_prd_labels[i] == 1 else (1*weights[i]) for i in range(len(y))]) + 0.0000001
lt_prd_labels = [1 if check_val <= split_val else -1 for check_val in rnd_column]
lt_err = sum([0 if y[i]*lt_prd_labels[i] == 1 else (1*weights[i]) for i in range(len(y))]) + 0.0000001
if gt_err < 0.5:
alpha = 0.5*np.log((1-gt_err)/gt_err)
weights = [weights[i]*np.exp(-alpha*y[i]*gt_prd_labels[i]) for i in range(len(y))]
weak_learner = [rnd_column_indx,split_val,'gt']
return weak_learner, alpha, weights
elif lt_err < 0.5:
alpha = 0.5*np.log((1-lt_err)/lt_err)
weights = [weights[i]*np.exp(-alpha*y[i]*lt_prd_labels[i]) for i in range(len(y))]
weak_learner = [rnd_column_indx,split_val,'lt']
return weak_learner, alpha, weights
print ("No Weak Learner Found for feature " + str(rnd_column_indx))
return weak_learner,alpha,weights
Estimator Testing
To ensure the estimator has been properly coded, we compare the results on a couple datasets with the packaged adabooster in sklearn. Visualizing the results, we can see the estimator is performing as expected, with similiar results as the sklearn adabooster. The differences likely stem from our custom estimator not encorporating any intelligence in the decision stubs, simply naively selecting any that perform better than random as the weak learning, where sklearns adaboost utilizes decisiontree classifiers as their stubs which optimize the stub splitting based on information gain metrics.
# Code source: Gaël Varoquaux
# Andreas Müller
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
h = .02 # step size in the mesh
names = ["AdaBoost","IMLAdaBoost"]
classifiers = [
AdaBoostClassifier(),
IMLAdaBoostClassifier(n_estimators=25)
]
X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
random_state=1, n_clusters_per_class=1)
y = [-1 if tmp_y == 0 else 1 for tmp_y in y]
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
datasets = [make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
]
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds_cnt, ds in enumerate(datasets):
# preprocess dataset, split into training and test part
X, y = ds
y = [-1 if tmp_y == 0 else 1 for tmp_y in y]
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=.4, random_state=42)
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data")
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
# and testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
i += 1
# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf.fit(X_train, y_train)
score = clf.score(X_test,y_test)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
if hasattr(clf, "decision_function"):
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
# Put the result into a color plot
Z = Z.reshape(xx.shape)
ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)
# Plot also the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
# and testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
alpha=0.6)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
if ds_cnt == 0:
ax.set_title(name)
ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
size=15, horizontalalignment='right')
i += 1
plt.tight_layout()
plt.show()
Customer Churn Exploratory Data Analysis
Below is a preliminary Exploratory Data Analysis of the customer churn data to help discover any data inconsistencies and provide an intuition for developing a model of customer churn.
df = pd.read_csv("churn.csv")
df.head()
| State | Account Length | Area Code | Phone | Int'l Plan | VMail Plan | VMail Message | Day Mins | Day Calls | Day Charge | ... | Eve Calls | Eve Charge | Night Mins | Night Calls | Night Charge | Intl Mins | Intl Calls | Intl Charge | CustServ Calls | Churn? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KS | 128 | 415 | 382-4657 | no | yes | 25 | 265.1 | 110 | 45.07 | ... | 99 | 16.78 | 244.7 | 91 | 11.01 | 10.0 | 3 | 2.70 | 1 | False |
| 1 | OH | 107 | 415 | 371-7191 | no | yes | 26 | 161.6 | 123 | 27.47 | ... | 103 | 16.62 | 254.4 | 103 | 11.45 | 13.7 | 3 | 3.70 | 1 | False |
| 2 | NJ | 137 | 415 | 358-1921 | no | no | 0 | 243.4 | 114 | 41.38 | ... | 110 | 10.30 | 162.6 | 104 | 7.32 | 12.2 | 5 | 3.29 | 0 | False |
| 3 | OH | 84 | 408 | 375-9999 | yes | no | 0 | 299.4 | 71 | 50.90 | ... | 88 | 5.26 | 196.9 | 89 | 8.86 | 6.6 | 7 | 1.78 | 2 | False |
| 4 | OK | 75 | 415 | 330-6626 | yes | no | 0 | 166.7 | 113 | 28.34 | ... | 122 | 12.61 | 186.9 | 121 | 8.41 | 10.1 | 3 | 2.73 | 3 | False |
5 rows × 21 columns
df['Churn?'].value_counts()
False 2850
True 483
Name: Churn?, dtype: int64
The dataset has 21 features containing information about customer geographic location, the type of service they've subscribed to, their usage and interactions with customer service. This information is labelled with whether that user ended up quitting their subscription. The dataset is unbalanced, with 2850 retained customers compared to 483 churned. There are a couple features that are unlikely to provide usable information, like phone number and area code, so they are removed from the dataset.
filtered_features = ['Phone','Area Code']
df = df.drop(filtered_features, 1)
hists = df.hist(bins=50,figsize=(15, 10))
Exploring the distributions of the features, almost all of them following closely to normal distributions. The only exception being VMail message, with the majority of users having 0 VMail messages, likely because they have not subscribed to this service.
(df.groupby(['State','Churn?'])['Account Length'].count().unstack().sort_values(by=True, ascending = False)
.plot(kind='bar',stacked=True,figsize=(15, 5)))
<matplotlib.axes._subplots.AxesSubplot at 0x2085e7b8940>
Exploring the churn-rate per state in the above visualization, we can see that there is a wide spread between churn-rate amoung different states, with NJ and TX having high churn and IA and AK with barely any. This islikely due to the different competetion markets across states, and should provide powerful discriminating features in a churn classifier.
sns.pairplot(df, hue="Churn?")
<seaborn.axisgrid.PairGrid at 0x208460a64a8>
Reviewing the scatter matrix for each feature reveals the direct linear relationship between call minutes and their respective charges. These redundant features are removed from the dataset to prevent the potential negative influence of dependent features on the classifier. Although some features show a little seperating between churn labels, an obvious feature combination does not appear for discriminating churn customers. A higher dimensional classifier with further combinations of the features will required to build an adequate classifier.
filtered_features = ['Day Mins', 'Eve Mins', 'Night Mins', 'Intl Mins']
df = df.drop(filtered_features,1)
df.head()
| State | Account Length | Int'l Plan | VMail Plan | VMail Message | Day Calls | Day Charge | Eve Calls | Eve Charge | Night Calls | Night Charge | Intl Calls | Intl Charge | CustServ Calls | Churn? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KS | 128 | no | yes | 25 | 110 | 45.07 | 99 | 16.78 | 91 | 11.01 | 3 | 2.70 | 1 | False |
| 1 | OH | 107 | no | yes | 26 | 123 | 27.47 | 103 | 16.62 | 103 | 11.45 | 3 | 3.70 | 1 | False |
| 2 | NJ | 137 | no | no | 0 | 114 | 41.38 | 110 | 10.30 | 104 | 7.32 | 5 | 3.29 | 0 | False |
| 3 | OH | 84 | yes | no | 0 | 71 | 50.90 | 88 | 5.26 | 89 | 8.86 | 7 | 1.78 | 2 | False |
| 4 | OK | 75 | yes | no | 0 | 113 | 28.34 | 122 | 12.61 | 121 | 8.41 | 3 | 2.73 | 3 | False |
mapping = {'yes': 1, 'no': 0}
df = df.replace({"Int'l Plan": mapping, 'VMail Plan': mapping})
#One Hot Encode States for classifer
state_df = df['State'].str.get_dummies()
df = df.drop(['State'],1)
df = pd.concat([df,state_df],axis=1)
cols = ['Account Length', "Int'l Plan", 'VMail Plan', 'VMail Message',
'Day Calls', 'Day Charge', 'Eve Calls', 'Eve Charge', 'Night Calls',
'Night Charge', 'Intl Calls', 'Intl Charge', 'CustServ Calls',
'AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI',
'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN',
'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH',
'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA',
'WI', 'WV', 'WY','Churn?']
df = df[cols]
df.columns
Index(['Account Length', 'Int'l Plan', 'VMail Plan', 'VMail Message',
'Day Calls', 'Day Charge', 'Eve Calls', 'Eve Charge', 'Night Calls',
'Night Charge', 'Intl Calls', 'Intl Charge', 'CustServ Calls', 'AK',
'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA',
'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO',
'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK',
'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI',
'WV', 'WY', 'Churn?'],
dtype='object')
Model Development
Transforming the cleaned dataset into training and testing datasets, we can build the classifier by tuning the feature selector and estimator count with grid search. The weights are also redistributed to take into account the imbalance in the dataset.
train, test = train_test_split(df, test_size = 0.2)
X_train = train.as_matrix()[:,0:len(train.columns)-1]
y_train = train.as_matrix()[:,-1].astype(int)
y_train[y_train == True] = 1
y_train[y_train == False] = -1
X_test = test.as_matrix()[:,0:len(test.columns)-1]
y_test = test.as_matrix()[:,-1].astype(int)
y_test[y_test == True] = 1
y_test[y_test == False] = -1
feature_selector = SelectKBest(k=5)
weights = [2*1/len(y_train) if y_train[i] == 1 else 1/(2*len(y_train)) for i in range(len(y_train))]
weights = [wgt/sum(weights) for wgt in weights]
clf = IMLAdaBoostClassifier(n_estimators=10,weights=weights)
clf_pipe = Pipeline([('feature_selector', feature_selector), ('clf', clf)])
params = dict(feature_selector__k=[4,5,6,7,8],
clf__n_estimators=[100,125,150,175,200])
iml_clf = GridSearchCV(clf_pipe, param_grid=params)
iml_clf.fit(X_train,y_train)
iml_clf.best_estimator_
Pipeline(steps=[('feature_selector', SelectKBest(k=5, score_func=<function f_classif at 0x000001B741D61BF8>)), ('clf', IMLAdaBoostClassifier(n_estimators=100,
weights=[0.00026150627615061507, 0.00026150627615061507, 0.00026150627615061507, 0.00026150627615061507, 0.00026150627615061507, 0.00026150...3, 0.0010460251046024603, 0.00026150627615061507, 0.00026150627615061507, 0.00026150627615061507]))])
print ("score = " + str(iml_clf.score(X_test,y_test)))
preds = iml_clf.predict(X_test)
confusion_matrix(y_test,preds)
score = 0.842578710645
array([[483, 87],
[ 18, 79]])
The resulting classifier takes the top 5 features and applies 100 weak learners to develop the final classifier, with a classification score of 84%. The classifier over predicts the number of churn clients, while under estimating the non-churning clients. This is the desired result of the classifier, with the specification stating we are more concerned with identifying potential churn clients over predicting non-churning ones.
feature_selector = SelectKBest(k=5)
clf = AdaBoostClassifier()
clf_pipe = Pipeline([('feature_selector', feature_selector), ('clf', clf)])
params = dict(feature_selector__k=[4,5,6,7,8],
clf__n_estimators=[100,125,150,175,200])
skl_clf = GridSearchCV(clf_pipe, param_grid=params)
skl_clf.fit(X_train,y_train)
skl_clf.best_estimator_
Pipeline(steps=[('feature_selector', SelectKBest(k=8, score_func=<function f_classif at 0x000001B741D61BF8>)), ('clf', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
learning_rate=1.0, n_estimators=100, random_state=None))])
print ("score = " + str(skl_clf.score(X_test,y_test)))
preds = skl_clf.predict(X_test)
confusion_matrix(y_test,preds)
score = 0.871064467766
array([[549, 21],
[ 65, 32]])
When compared to the sklearn implementation of adaboost, the classifiers have similiar performance, with sklearn's classifier having 3% more classifcation accuracy. This gives confidence to the ability of our custom classifier to adequality classify real problems, but also indicates the existance of improvements we can embed into our classifier.
iml_features = iml_clf.best_estimator_.named_steps['feature_selector'].scores_
iml_top_features = sorted(range(len(iml_features)), key=lambda i: iml_features[i],reverse=True)[0:10]
df.columns[iml_top_features]
Index(['Int'l Plan', 'CustServ Calls', 'Day Charge', 'VMail Plan',
'Eve Charge', 'VMail Message', 'Intl Charge', 'TX', 'Intl Calls', 'MS'],
dtype='object')
Reviewing the feature importance scores, we can see that the plan usage and calls to the service center are important features, along with a couple of states that show high rates of churn. This information could be used to target these specific attributes for improvement in the telcomunication product.
Investment Plan
With the developed classifier, we can use its prediction power to develop an investment decision planner for deciding whether to invest into retention plans for certain customers. The following script provides the tools to transform new customer data to be fed into the investment decider, which takes the cost of retention, expected return on retained customers and the probability of customer churn from the developed classifier, and returns the expect monetary return from the individual retention investment. If the return is positive, an investment to retain the customer is recommended.
def data_transforms(df):
filtered_features = ['Phone','Area Code','Day Mins', 'Eve Mins', 'Night Mins', 'Intl Mins']
df = df.drop(filtered_features,1)
mapping = {'yes': 1, 'no': 0}
df = df.replace({"Int'l Plan": mapping, 'VMail Plan': mapping})
state_df = df['State'].str.get_dummies()
df = df.drop(['State'],1)
df = pd.concat([df,state_df],axis=1)
cols = ['Account Length', "Int'l Plan", 'VMail Plan', 'VMail Message',
'Day Calls', 'Day Charge', 'Eve Calls', 'Eve Charge', 'Night Calls',
'Night Charge', 'Intl Calls', 'Intl Charge', 'CustServ Calls',
'AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI',
'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN',
'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH',
'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA',
'WI', 'WV', 'WY']
for cl in cols:
if cl not in df.keys():
df[cl] = 0
df = df[cols]
return df
def investment_decision(cost,roi,customer_data):
inv_dec = []
churn_prob = iml_clf.decision_function(customer_data)
for exp_churn in churn_prob:
if exp_churn < 0:
expected_return = -cost
else:
expected_return = (roi*exp_churn-cost)
inv_dec.append(expected_return)
return inv_dec
df = pd.read_csv("new_customers.csv")
df = data_transforms(df)
rslt = investment_decision(50,250,df)
for i,cust in enumerate(rslt):
if cust > 0:
print("Invest in retain customer " + str(i))
Invest in retain customer 15
Invest in retain customer 198
Invest in retain customer 542
Invest in retain customer 546
Invest in retain customer 694
Invest in retain customer 974
Invest in retain customer 978
Invest in retain customer 985
Invest in retain customer 1192
Invest in retain customer 1407
Invest in retain customer 1472
Invest in retain customer 1610
Invest in retain customer 2076
Invest in retain customer 2614
Invest in retain customer 2660
Invest in retain customer 2732
Invest in retain customer 2786
Invest in retain customer 2942
Invest in retain customer 3322
Future Work
Through comparing our classifier with those implemented in sklearn, it's clear that some optimization exist for improvement, the most obvious being added intelligence in the decision stub thresholding and weak learner development. Other work will include the validation of our investment planner and its success at different levels of investment cost and expected customer returns.
Bibliography
https://classroom.udacity.com/courses/ud675/lessons/367378584/concepts/3675485950923#
http://scikit-learn.org/stable/documentation.html
http://pandas.pydata.org/pandas-docs/stable/
http://seaborn.pydata.org/


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Gabbert tosses 3 TDs, Bester runs for 2 in Miami (Ohio) win
OXFORD, Ohio (AP) - Brett Gabbert passed for three touchdowns, Jaylon Bester ran for two scores and Miami (Ohio) used big plays to roll over Bowling Green 44-3 on Wednesday night.
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Mike Brown gave Miami a quick lead with a 79-yard interception return within the first two minutes of the game.
The victory (6-4, 5-1) was the fourth in a row for the RedHawks and strengthened their grip on first place in the Mid-American Conference East Division lead with two games remaining.
Bryson Denley rushed for 105 yards for the Falcons (3-7, 2-4) but Grant Loy was intercepted three times, twice by Travion Banks. Miami outgained Bowling Green 425-275.
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<?xml version="1.0"?>
<openejb>
<Container id="Default CMP Container" ctype="CMP_ENTITY">
Global_TX_Database c:/my/app/conf/postgresql.cmp_global_database.xml
Local_TX_Database c:/my/app/conf/postgresql.cmp_local_database.xml
</Container>
<Connector id="Default JDBC Database">
JdbcDriver org.postgresql.Driver
JdbcUrl jdbc:postgresql://localhost/mydb
UserName username
Password password
</Connector>
<SecurityService id="Default Security Service"/>
<TransactionService id="Default Transaction Manager"/>
<Deployments jar="c:/my/app/employee.jar"/>
<Deployments dir="beans/" />
</openejb>
Configuration
Short Overview
Configuration Properties
-
openejb.home - OpenEJB home (installation) directory path. All relative paths are resolved against the property unless openejb.base is set. Unless set, the value is assigned to the user.dir Java property.
-
openejb.base - OpenEJB base directory path. If set, the directory pointed by the property is searched for resources before openejb.home.
-
openejb.configuration - OpenEJB configuration file path.
-
openejb.loader - OpenEJB loader that’s responsible for loading EJBs. There are 3 different loader types:
-
tomcat-webapp - set it when inside of Tomcat scoped at just the webapp, aka. Collapsed EAR tomcat - set it when inside of Tomcat scoped for all webapps to share system (also: bootstrap) ** embedded (also: noload)
-
-
openejb.configurator (default: org.openejb.alt.config.ConfigurationFactory ) - a class that builds org.openejb.alt.assembler.classic.OpenEjbConfiguration object; implements the org.openejb.alt.assembler.classic.OpenEjbConfigurationFactory interface
-
openejb.descriptors.output - possible values: true|false - When set OpenEJB saves deployment descriptors - ejb-jar.xml and openejb-jar.xml
Configuration File
Show a config file with the elements hyperlinked.
Basic Layout
Basically, openejb.base is the source for 100% of all configuration information and third party config files (log4j, castor, instantdb, whatever). This includes finding where the, possibly many, entries in the openejb.conf point. The openejb.home is where the code loading OpenEJB will look for all the OpenEJB libraries. Usually openejb.base is not explicitly set and defaults to the value of openejb.home, so many people are used to only dealing with openejb.home.
The point of having and openejb.base and openejb.home was basically to allow several independently configured instances of OpenEJB running on a system (perhaps embedded in Swing apps, in Tomcat, running as a standalone Server, or even in Groovy as Mr. Strachan did!) but without the need to copy all the OpenEJB system libraries everywhere.
openejb.home * can be set explicitly via a system property. * if not set it default’s to user.dir, which is the current working directory.
openejb.base * can be set explicitly via a system property. * If not set it default’s to openejb.home.
openejb.configuration * can be set to explicitly point to the file containing your configuration. * If set to a relative path, we first look in user.dir/your-conf-file, then in openejb.base/your-conf-file * If not set we check in openejb.base/conf/openejb.conf * If no conf file is found, we create one in openejb.base/conf/openejb.conf
relative paths in openejb.conf * Deployment entries are resolved relative to openejb.base. * Containers use openejb.base to resolve their own config files. For example, Castor JDO to loads the database.xml and all other files from the openejb.base directory. * Resource adapters that are embedded usually have config files of their own and are also loaded from the openeb.base.
log files * The log4.configuration file is resolved relative to openejb.base. * The properties in the config file that point to files are also resolved relative to openejb.base.
OpenEJB libraries * The jars in the lib and dist directories under openejb.home are added to the classpath.
Summary
A summary of the above in a different notation:
openejb.home = user.dir (can be set explicitly)
openejb.base = openejb.home (can be set explicitly)
openejb.conf = openejb.base/conf/openejb.conf (can be set explicitly)
logging.conf = openejb.base/conf/logging.conf (can be set explicitly)
deployments = paths listed in openejb.conf (relative paths resolved from openejb.base)
Classpath includes openejb.home/lib and openejb.home/dist
Example layout
In this one the openejb.home and openejb.base are set, everything else is defaulted. The openejb.conf file as been updated to point to the ejb jars by name (abc-ejbs.jar and xyz-ejbs.jar).
An example layout:
/usr/local/openejb (openejb.home)
/usr/local/openejb/lib (in classpath)
/usr/local/openejb/dist (in classpath)
/home/jsmith/foo_app (openejb.base)
/home/jsmith/foo_app/conf/openejb.conf
/home/jsmith/foo_app/conf/logging.conf
/home/jsmith/foo_app/abc-ejbs.jar (Deployment entry in openejb.conf)
/home/jsmith/foo_app/xyz-ejbs.jar (Deployment entry in openejb.conf)
/home/jsmith/foo_app/logs/
Another Example layout
In this example openejb.home and openejb.base are setup as well as the explicit paths for the openejb and log4j configuration files.
An example layout:
/usr/local/openejb (openejb.home)
/usr/local/openejb/lib (in classpath)
/usr/local/openejb/dist (in classpath)
/home/jsmith/foo_app (openejb.base)
/home/jsmith/foo_app/openejb.xml (openejb.configuration)
/home/jsmith/foo_app/abc-ejbs.jar (Deployment entry in openejb.xml)
/home/jsmith/foo_app/xyz-ejbs.jar (Deployment entry in openejb.xml)
/home/jsmith/foo_app/log4j.conf (log4j.configuration)
/home/jsmith/foo_app/mylogs/ (logging dir as defined in log4j.conf)


We incorporate diverse services for designing, development and marketing. We understand business. That's why we begin every project with a workshop, that helps in crafting a one-of-a-kind, unique strategy that is designed to help you win.
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A simple HTTP API into a GeoIP database
The source code for this service is available on Github
A docker image is available on Docker Hub
Lookup the country and ASN of an IP
$ curl -s http://localhost:8080/ip/<IP_ADDRESS> | jq { "country": "US", "asn": 3378 }


Situation Aware Opinion Mining for the Business Intelligence domain
A PhD project, being developed by Miguel Fernandes for the University of Vigo, under the supervision of Ana Fernández Vilas and Rebeca Díaz Redondo.
Directly from twitter, with your own API Keys. Tweets are extracted using the Search API, and in the near future, the Stream API for live events.
Texts are cleaned, trimmed, separated, tokenized, lemmatized and processed, using several Natural Language Processing (NLP) techniques available.
Do this message express happiness? sadness? is the customer satisfied? did our decision had a bad impact on our users? for how long? Questions aimed to be answered by the platform.
Convert data into information. Evolution graphs, comparison charts, exportable tables. All on your own desktop browser, smartphone or tablet. One click away.
No, this is a work in progress, but we will be releasing some nightlies soon. We are working at a fast pace developing the platform, but as a work in progress, you should be aware that bugs will be all over the place. Features will be added on a weekly basis, before we enter into the feature freeze phase.
Sure!. You will need a server with internet connection and docker installed, and everything else will be taken care of. Be sure to follow the instructions on our github site, and you will get a MongoDB, an Eureka registry and the full marble platform using a single command from the shell.
You can find the documentation in the project's Wiki, and in the main README file of the project.
Not at all. We are using libraries and software developed by other people, and we will give credit to them appropiately in our third party section (still to be developed). If you think we are using some of your software inappropriately, please let us know.
We are releasing all the software developed for this project under the Apache license 2.0. Click here for more information about it.
Please open an issue on github to let me know.


--- layout: default title: Who am I?! ---
I am an Indian. Having lived in India for most of my student life, I moved to London to work as a Software Engineer in Google. In my spare time, I love to read blogs, play chess, read/write on Quora and learn about new Software technologies. I also play keyboard when the mood is right.


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@import models.FileNameForm @(form: Form[models.FileNameForm])(implicit request: MessagesRequestHeader, flash: Flash) @exportMsg = @{flash.get("exportMsg").getOrElse("")} @exportError = @{flash.get("exportError").getOrElse("")} @main("Welcome to Snooker O") {
Use this page to export your final results as HTML files for publishing on your own site
@helper.form(action = routes.ResultsController.exportResultsSubmit) {Sorry, this feature is still in development.
For now your files will be written to "C:\Orienteering" unless the permissions are blocked.
After the export, this page will go back to the summary results page.
@exportMsg
} else { @if(exportError.nonEmpty){@exportError
} }


FeathersJS Auth Recipe：使用混合身份验证创建端点¶
The Auk release of FeathersJS includes a powerful new authentication suite built on top of PassportJS. It can be customized to handle almost any app’s authentication requirements. In this guide, we’ll look at how to handle a fairly common auth scenario: Sometimes an endpoint needs to serve different information depending on whether the user is authenticated. An unauthenticated user might only see public records. An authenticated user might be able to see additional records.
Setup the Authentication Endpoint¶
To get started, we need a working authentication setup. Below is a
default configuration and authentication.js
. They contain the same
code generated by the
feathers-cli. You can create the
below setup using the following terminal commands:
npm install -g @feathersjs/cli
mkdir feathers-demo-local; cd feathers-demo-local
or a folder name you prefer.feathers generate app
use the default prompts.feathers generate authentication
Select
Username + Password (Local)
when prompted for a provider.Select the defaults for the remaining prompts.
config/default.json:
{
"host": "localhost",
"port": 3030,
"public": "../public/",
"paginate": {
"default": 10,
"max": 50
},
"authentication": {
"secret": "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",
"strategies": [
"jwt",
"local"
],
"path": "/authentication",
"service": "users",
"jwt": {
"header": {
"typ": "access"
},
"audience": "https://yourdomain.com",
"subject": "anonymous",
"issuer": "feathers",
"algorithm": "HS256",
"expiresIn": "1d"
},
"local": {
"entity": "user",
"service": "users",
"usernameField": "email",
"passwordField": "password"
}
},
"nedb": "../data"
}
src/authentication.js:
'use strict';
const authentication = require('@feathersjs/authentication');
const jwt = require('@feathersjs/authentication-jwt');
const local = require('@feathersjs/authentication-local');
module.exports = function () {
const app = this;
const config = app.get('authentication');
app.configure(authentication(config));
app.configure(jwt());
app.configure(local(config.local));
app.service('authentication').hooks({
before: {
create: [
authentication.hooks.authenticate(config.strategies)
],
remove: [
authentication.hooks.authenticate('jwt')
]
}
});
};
Set up a “Mixed Auth” Endpoint¶
Now we need to setup an endpoint to handle both unauthenticated and
authenticated users. For this example, we’ll use the /users
service
that was already created by the authentication generator. Let’s suppose
that our application requires that each user
record will contain a
public
boolean property. Each record will look something like this:
{
id: 1,
email<EMAIL_ADDRESS>password: "password",
public: true
}
If a user
record contains public: true
, then unauthenticated
users should be able to access it. Let’s see how to use the iff
and
else
conditional hooks from
`feathers-hooks-common
<https://feathers-plus.github.io/v1/feathers-hooks-common/>`_
to make this happen. Be sure to read the
`iff hook API docs
<https://feathers-plus.github.io/v1/feathers-hooks-common/#iff>`_
and
`else hook API docs
<https://feathers-plus.github.io/v1/feathers-hooks-common/#else>`_
if you haven’t, yet.
We’re going to use the iff
hook to authenticate users only if a
token is in the request. The feathers-authentication-jwt plugin, which we used in
src/authentication.js
, includes a token extractor. If a request
includes a token, it will automatically be available inside the
context
object at context.params.token
.
src/services/users/users.hooks.js (This example only shows the
find
method’s before
hooks.)
'use strict';
const { authenticate } = require('@feathersjs/authentication').hooks;
const commonHooks = require('feathers-hooks-common');
module.exports = {
before: {
find: [
// If a token was included, authenticate it with the `jwt` strategy.
commonHooks.iff(
context => context.params.token,
authenticate('jwt')
// No token was found, so limit the query to include `public: true`
).else( context => Object.assign(context.params.query, { public: true }) )
]
}
};
Let’s break down the above example. We setup the find
method of the
/users
service with an iff
conditional before hook:
iff(
context => context.params.token,
authenticate(‘jwt’)
)
For this application, the above snippet is equivalent to the snippet, below.
context => {
if (context.params.token) {
return authenticate(‘jwt’)
} else {
return Promise.resolve(context)
}
}
The iff
hook is actually more capable than the simple demonstration,
above. It can handle an async predicate expression. This would be
equivalent to being able to pass a promise
inside the if
statement’s parentheses. It also allows us to chain an .else()
statement, which will run if the predicate evaluates to false.
.else(
context => {
Object.assign(context.params.query, { public: true })
return context
)
The above statement simply adds public: true
to the query
parameters. This limits the query to only find user
records that
have the public
property set to true
.
Wrapping Up¶
With the above code, we’ve successfully setup a /users
service that
responds differently to unauthenticated and authenticated users. We used
the context.params.token
attribute to either authenticate a user or
to limit the search query to only public users. If you become familiar
with the Common Hooks
API,
you’ll be able to solve almost any authentication puzzle.


--- layout: page title: About header-img: "img/about-bg.png" ---
Entrepreneur, Occasional Adventurer, Home Gardener, Compulsive Learner. Based in Cambridge, MA.


Installation
Place the following lines in your <head> area.
<script data-evalhtmldec src="../build/htmldecorators.js"></script>
<script src="../build/htmldecorators-std.js"></script>
<link rel="stylesheet" href="../build/htmldecorators.css">
If you want to add the standard components you have add the following to your document.
<!-- This will add the decorators which renders the component (needs to be loaded before) -->
<script src="../build/htmldecorators-std-components.js"></script>
<!-- This will load all components at once (in body-tag) -->
<template data-htmldec>
@Init
@LoadHTMLStack(
id=appStack,
combined0=../build/htmldecorators-std-components.html
)
</template>


ONCE in these parts, and not so long gone neither, there was a fool that wanted to buy a pottle o' brains, for he was ever getting into scrapes through his foolishness, and being laughed at by everyone. Folk told him that he could get everything he liked from the wise woman that lived on the top o' the hill, and dealt in potions and herbs and spells and things, and could tell thee all as'd come to thee or thy folk. So he told his mother, and asked her if he could seek the wise woman and buy a pottle o' brains.
'That ye should,' says she; 'thou'st sore need o' them, my son; and if I should die, who'd take care o' a poor fool such's thou, no more fit to look after thyself than an unborn baby? But mind thy manners, and speak her pretty, my lad; for they wise folk are gey and light mispleased.'
So off he went after his tea, and there she was, sitting by the fire, and stirring a big pot.
'Good e'en, missis,' says he, 'it's a fine night.'
'Aye,' says she, and went on stirring.
'It'll maybe rain,' says he, and fidgeted from one foot to t'other.
'Maybe,' says she.
'And m'appen it won't,' says he, and looked out o' the window.
'M'appen,' says she.
And he scratched his head and twisted his hat.
'Well,' says he, 'I can't mind nothing else about the weather, but let me see; the crops are getting on fine.'
'Fine,' says she.
'And -- and -- the beasts is fattening,' says he.
'They are,' says she.
'And -- and -- ' says he, and comes to a stop -- 'I reckon we'll tackle business now, having done the polite like. Have you any brains for to sell?'
'That depends,' says she; 'if thou wants king's brains, or soldier's brains, or schoolmaster's brains, I dinna keep 'em.'
'Hout no,' says he, 'just ordinary brains -- fit for any fool --same as everyone has about here; something clean common-like.'
'Aye so,' says the wise woman, 'I might manage that, if so be thou'lt help thyself.'
'How's that for, missis?' says he.
'Jest so,' says she, looking in the pot: 'bring me the heart of the thing thou likest best of all, and I'll tell thee where to get thy pottle o' brains.'
'But,' says he, scratching his head, 'how can I do that?'
'That's no for me to say,' says she; 'Find out for thyself, my lad! If thou doesn't want to be a fool all thy days. But thou'll have to read me a riddle so as I can see thou'st brought the right thing, and if thy brains is about thee. And I've something else to see to,' says she, 'so gode'en to thee,' and she carried the pot away with her into the back place.

So he killed his pig, and next day off he went to the wise woman's cottage, and there she sat, reading in a great book.
'Gode'en, missis,' says he, 'I've brought thee the heart o' the thing I like the best of all; and I put it hapt in paper on the table.'
'Aye so?' says she, and looked at him through her spectacles. 'Tell me this then, what runs without feet?'
He scratched his head, and thought, and thought, but he couldn't tell.
'Go thy ways,' she said, 'thou'st not fetched me the right thing yet. I've no brains for thee today.' And she clapt the book together, and turned her back.
So off the fool went to tell his mother.
But as he got nigh the house, out came folk running to tell him that his mother was dying.
And when he got in, his mother only looked at him and smiled as if to say she could leave him with a quiet mind, since he had got brains enough now to look after himself -- and then she died.
So down he sat and the more he thought about it the badder he felt. He minded how she'd nursed him when he was a tiddy brat, and helped him with his lessons, and cooked his dinners, and mended his clouts, and bore with his foolishness; and he felt sorrier and sorrier, while he began to sob and greet.
'Oh, mother, mother!' says he, 'who'll take care of me now? Thou shouldn't have left me alone, for I liked thee better than everything!'
And as he said that he thought of the words of the wise woman. 'Hi, yi!' says he, 'must I take mother's heart to her?'
'No! I can't do that,' says he. 'What'll I do? What'll I do to get that pottle o' brains, now I'm alone in the world?' So he thought and thought and thought, and next day he went and borrowed a sack, and bundled his mother in, and carried it on his shoulder up to the wise woman's cottage.
'Gode'en, missis,' says he, 'I reckon I've fetched thee the right thing this time, surely', and he plumped the sack down kerflap! in the doorsill.
'Maybe,' says the wise woman, 'but read me this, now, what's yellow and shining but isn't gold?'
And he scratched his head, and thought and thought, but he couldn't tell.
'Thou'st not hit the right thing, my lad,' says she.
'See there!' says he, and set down by the roadside and greets.
'I've lost the only two things as I cared for, and what else can I find to buy a pottle o' brains with!' and he fair howled, till the tears ran down into his mouth. And up came a lass that lived near at hand, and looked at him.
'What's up with thee, fool?' says she.

'That's bad,' says she; 'and haven't thee anybody to look after thee?'
'No,' says he, 'and I canna buy my pottle o' brains, for there's nothing I like best left.'
'What art talking about?' says she.
And down she sets by him, and he told her all about the wise woman and the pig, and his mother and the riddles, and that he was alone in the world.
'Well,' says she, 'I wouldn't mind looking after thee myself.'
'Could thee do it?' says he.
'Ou, aye!' says she; 'Folks say as fools make good husbands, and I reckon I'll have thee, if thou'rt willing.'
'Can'st cook?' says he.
'Aye, I can,' says she.
'And scrub?' says he.
'Surely,' says she.
'And mend my clouts?' says he.
'I can that,' says she.
'I reckon thou'lt do then as well as anybody,' says he; 'but what'll I do about this wise woman?'
'Oh, wait a bit,' says she, 'something may turn up, and it'll not matter if thou'rt a fool, so long'st thou'st got me to look after thee.'
'That's true,' says he, and off they went and got married. And she kept his house so clean and neat, and cooked his dinner so fine, that one night he says to her: 'Lass, I'm thinking I like thee best of everything after all.'
'That's good hearing,' says she, 'and what then?'
'Have I got to kill thee, dost think, and take thy heart up to the wise woman for that pottle o' brains?'
'Law, no!' says she, looking skeered, 'I winna have that. But see here; thou didn't cut out thy mother's heart, did thou?'
'No, but if I had, maybe I'd have got my pottle o' brains,' says he.
'Not a bit of it,' says she; 'just thou take me as I be, heart and all, and I'll wager I'll help thee read the riddles.'
'Can thee so?' says he, doubtful like; 'I reckon they're too hard for women folk.'
'Well,' says she, 'let's see now. Tell me the first.'
'What runs without feet?' says he.
'Why, water!' says she.
'It do,' says he, and scratched his head.
'And what's yellow and shining but isn't gold?'
'Why, the sun!' says she.
'Faith, it be!' says he. 'Come, we'll go up to the wise woman at once', and off they went.
And as they came up the pad, she was sitting at the door, twining straws.
'Gode'en, missis,' says he.
'Gode'en, fool,' says she.
'I reckon I've fetched thee the right thing at last,' says he.
The wise woman looked at them both, and wiped her spectacles.
'Canst tell me what that is as has first no legs, and then two legs, and ends with four legs?'
And the fool scratched his head and thought and thought, but he couldn't tell.
And the lass whispered in his ear: 'It's a tadpole.'
'M'appen,' says he; then, 'it may be a tadpole, missis.'
The wise woman nodded her head.
'That's right,' says she, 'and thou'st got thy pottle o' brains already.'
'Where are they?' says he, looking about and feeling in his pockets.
'In thy wife's head,' says she. 'The only cure for a fool is a good wife to look after him, and that thou'st got, so gode'en to thee!' And with that she nodded to them, and up and into the house.
So they went home together, and he never wanted to buy a pottle o' brains again, for his wife had enough for both.


Getting started¶
Introduction¶
This section outlines what you need to get started using these resources and introduces the format and focuses of the self-guided course.
You will need access to a computer with a UNIX command line interface to perform the vast majority of the tutorials on this site. You can learn more about this in the Setting up a bioinformatics machine pages. If you are a participant in the Theory and practice in metabarcoding for biodiversity workshop 7th - 13th January 2021, you can completely skip this section.
If your bioinformatics machine is a remote server, we provide some general instructions on how to communicate with this server in the Accessing linux servers page.
The How to use these resources page gives an overview of the format we will use in all of the tutorials in these resources, so it is important to make sure you read this carefully before you start on the tutorials.
You might want to read a short discussion about The bioinformatics toolbox about our philosophy when it comes to pipelines, packages and choosing software.
Finally, the Command line bioinformatics pages give a introduction to using the UNIX command line interface for bioinformatics if you’re completely new to the terminal. This includes some useful commands and a little discussion about how to keep your data organised which might be of interest even if you’re familiar with the command line.
Once you’re happily set up with a suitable bioinformatics machine and ready to get going, you should head over to the Example data section.


Ktube Media Downloader is a Successor of My previous media downloader called Ultimate-Media-Downloader. With the hole re-write of code base and features and design. Ultimate Media Downloader is can be installed from Ubuntu Software Center Application Unlock fee is approx 3 USD.
Ktube Media Downloader is a freeware as it gives you ability to download two Urls daily and allow access to all other application features, in Order to break this limit you have to unlock the full version of application, To Unlock full version there is a In-App payment Method available with which you can securely pay application charge.
Dehradun,
Uttarakhand,
India.
Email:
<EMAIL_ADDRESS>Website:
www.ktechpit.com
Tel:
+91<PHONE_NUMBER>
(c) 2017 Ktechpit.com


Please choose your Parent theme
Blank Theme
Luma Theme
BRUSH
Theme Styling for Magento 2
Upload Design
Download Theme File


If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.
- dtype : data-type, optional
- Type to use in computing the mean. For integer inputs, the default is float64 ; for floating point inputs, it is the same as the input dtype.
- out : ndarray, optional
-
Alternate output array in which to place the result. The default
is
None
; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See doc.ufuncs for details. - keepdims : bool, optional
-
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray , however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.
- m : ndarray, see dtype parameter above
- If out=None , returns a new array containing the mean values, otherwise a reference to the output array is returned.
average : Weighted average std, var, nanmean, nanstd, nanvar
The arithmetic mean is the sum of the elements along the axis divided by the number of elements.
Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.
By default, float16 results are computed using float32 intermediates for extra precision.
>>> a = np.array([[1, 2], [3, 4]]) >>> np.mean(a) 2.5 >>> np.mean(a, axis=0) array([ 2., 3.]) >>> np.mean(a, axis=1) array([ 1.5, 3.5])
In single precision, mean can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.mean(a) 0.54999924
Computing the mean in float64 is more accurate:
>>> np.mean(a, dtype=np.float64) 0.55000000074505806
-
get_overlay_bounds
( overlay ) ¶ -
Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.
-
get_overlay_label
( overlay , default_label='' ) ¶ -
Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.
-
get_param_values
( onlychanged=False ) ¶ -
Return a list of name,value pairs for all Parameters of this object.
If onlychanged is True, will only return values that are not equal to the default value.
-
get_value_generator
= <functools.partial object> ¶
-
inspect_value
= <functools.partial object> ¶
-
instance
= <functools.partial object> ¶
-
message
( msg , *args , **kw ) ¶ -
Print msg merged with args as a message.
See Python’s logging module for details of message formatting.
-
params
( parameter_name=None ) ¶ -
Return the Parameters of this class as the dictionary {name: parameter_object}
Includes Parameters from this class and its superclasses.
-
pprint
( imports=None , prefix='\n ' , unknown_value='<?>' , qualify=False , separator='' ) ¶ -
Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y
-
print_param_defaults
( ) ¶ -
Print the default values of all cls’s Parameters.
-
print_param_values
( ) ¶ -
Print the values of all this object’s Parameters.
-
process_element
( element , key , **params ) ¶ -
The process_element method allows a single element to be operated on given an externally supplied key.
-
script_repr
( imports=[] , prefix=' ' ) ¶ -
Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y
-
search
( element , pattern ) ¶ -
Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.
The input may be a Layout, an Overlay type or a single Element.
-
set_default
( param_name , value ) ¶ -
Set the default value of param_name.
Equivalent to setting param_name on the class.
-
set_dynamic_time_fn
= <functools.partial object> ¶
-
set_param
= <functools.partial object> ¶
-
state_pop
( ) ¶ -
Restore the most recently saved state.
See state_push() for more details.
-
state_push
( ) ¶ -
Save this instance’s state.
For Parameterized instances, this includes the state of dynamically generated values.
Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().
Generally, this method is used by operations that need to test something without permanently altering the objects’ state.
-
verbose
( msg , *args , **kw ) ¶ -
Print msg merged with args as a verbose message.
See Python’s logging module for details of message formatting.
-
warning
( msg , *args , **kw ) ¶ -
Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.
See Python’s logging module for details of message formatting.
-
param Callable
-
class
holoviews.operation.timeseries.
rolling
( **params ) [source] ¶ -
Bases:
holoviews.core.operation.Operation
,holoviews.operation.timeseries.RollingBase
Applies a function over a rolling window.
-
param ObjectSelector
window_type
( allow_None=None, check_on_set=True, compute_default_fn=None, constant=False, default=None, instantiate=False, objects=[‘boxcar’, ‘triang’, ‘blackman’, ‘hamming’, ‘bartlett’, ‘parzen’, ‘bohman’, ‘blackmanharris’, ‘nuttall’, ‘barthann’, ‘kaiser’, ‘gaussian’, ‘general_gaussian’, ‘slepian’], pickle_default_value=True, precedence=None, readonly=False ) - The shape of the window to apply
-
param Callable
function
( allow_None=False, constant=False, instantiate=False, pickle_default_value=True, precedence=None, readonly=False ) - The function to apply over the rolling window.
-
param String
group
( allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False ) - The group string used to identify the output of the Operation. By default this should match the operation name.
-
param Integer
min_periods
( allow_None=True, bounds=None, constant=False, default=None, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, softbounds=None, time_dependent=False, time_fn=<Time Time00001> ) - Minimum number of observations in window required to have a value (otherwise result is NaN).
-
param Boolean
link_inputs
( allow_None=False, bounds=(0, 1), constant=False, default=False, instantiate=False, pickle_default_value=True, precedence=None, readonly=False ) - If the operation is dynamic, whether or not linked streams should be transferred from the operation inputs for backends that support linked streams. For example if an operation is applied to a DynamicMap with an RangeXY, this switch determines whether the corresponding visualization should update this stream with range changes originating from the newly generated axes.
-
param ObjectSelector
dynamic
( allow_None=None, check_on_set=True, compute_default_fn=None, constant=False, default=default, instantiate=False, objects=[‘default’, True, False], pickle_default_value=True, precedence=None, readonly=False ) - Whether the operation should be applied dynamically when a specific frame is requested, specified as a Boolean. If set to ‘default’ the mode will be determined based on the input type, i.e. if the data is a DynamicMap it will stay dynamic.
-
param Integer
rolling_window
( allow_None=False, bounds=None, constant=False, default=10, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, softbounds=None, time_dependent=False, time_fn=<Time Time00001> ) - The window size over which to operate.
-
param ClassSelector
input_ranges
( allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False ) - Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.
-
param List
streams
( allow_None=False, bounds=(0, None), constant=False, default=[], instantiate=True, pickle_default_value=True, precedence=None, readonly=False ) - List of streams that are applied if dynamic=True, allowing for dynamic interaction with the plot.
-
param Boolean
center
( allow_None=False, bounds=(0, 1), constant=False, default=True, instantiate=False, pickle_default_value=True, precedence=None, readonly=False ) - Whether to set the x-coordinate at the center or right edge of the window.
-
debug
( msg , *args , **kw ) ¶ -
Print msg merged with args as a debugging statement.
See Python’s logging module for details of message formatting.
-
defaults
( ) ¶ -
Return {parameter_name:parameter.default} for all non-constant Parameters.
Note that a Parameter for which instantiate==True has its default instantiated.
-
force_new_dynamic_value
= <functools.partial object> ¶
-
function
( a , axis=None , dtype=None , out=None , keepdims=<class numpy._globals._NoValue> ) ¶ -
Compute the arithmetic mean along the specified axis.
Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.
- a : array_like
- Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
- axis : None or int or tuple of ints, optional
-
Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
New in version 1.7.0.
If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.
- dtype : data-type, optional
- Type to use in computing the mean. For integer inputs, the default is float64 ; for floating point inputs, it is the same as the input dtype.
- out : ndarray, optional
-
Alternate output array in which to place the result. The default
is
None
; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See doc.ufuncs for details. - keepdims : bool, optional
-
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray , however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.
- m : ndarray, see dtype parameter above
- If out=None , returns a new array containing the mean values, otherwise a reference to the output array is returned.
average : Weighted average std, var, nanmean, nanstd, nanvar
The arithmetic mean is the sum of the elements along the axis divided by the number of elements.
Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.
By default, float16 results are computed using float32 intermediates for extra precision.
>>> a = np.array([[1, 2], [3, 4]]) >>> np.mean(a) 2.5 >>> np.mean(a, axis=0) array([ 2., 3.]) >>> np.mean(a, axis=1) array([ 1.5, 3.5])
In single precision, mean can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.mean(a) 0.54999924
Computing the mean in float64 is more accurate:
>>> np.mean(a, dtype=np.float64) 0.55000000074505806
-
get_overlay_bounds
( overlay ) ¶ -
Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.
-
get_overlay_label
( overlay , default_label='' ) ¶ -
Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.
-
get_param_values
( onlychanged=False ) ¶ -
Return a list of name,value pairs for all Parameters of this object.
If onlychanged is True, will only return values that are not equal to the default value.
-
get_value_generator
= <functools.partial object> ¶
-
inspect_value
= <functools.partial object> ¶
-
instance
= <functools.partial object> ¶
-
message
( msg , *args , **kw ) ¶ -
Print msg merged with args as a message.
See Python’s logging module for details of message formatting.
-
params
( parameter_name=None ) ¶ -
Return the Parameters of this class as the dictionary {name: parameter_object}
Includes Parameters from this class and its superclasses.
-
pprint
( imports=None , prefix='\n ' , unknown_value='<?>' , qualify=False , separator='' ) ¶ -
Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y
-
print_param_defaults
( ) ¶ -
Print the default values of all cls’s Parameters.
-
print_param_values
( ) ¶ -
Print the values of all this object’s Parameters.
-
process_element
( element , key , **params ) ¶ -
The process_element method allows a single element to be operated on given an externally supplied key.
-
script_repr
( imports=[] , prefix=' ' ) ¶ -
Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y
-
search
( element , pattern ) ¶ -
Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.
The input may be a Layout, an Overlay type or a single Element.
-
set_default
( param_name , value ) ¶ -
Set the default value of param_name.
Equivalent to setting param_name on the class.
-
set_dynamic_time_fn
= <functools.partial object> ¶
-
set_param
= <functools.partial object> ¶
-
state_pop
( ) ¶ -
Restore the most recently saved state.
See state_push() for more details.
-
state_push
( ) ¶ -
Save this instance’s state.
For Parameterized instances, this includes the state of dynamically generated values.
Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().
Generally, this method is used by operations that need to test something without permanently altering the objects’ state.
-
verbose
( msg , *args , **kw ) ¶ -
Print msg merged with args as a verbose message.
See Python’s logging module for details of message formatting.
-
warning
( msg , *args , **kw ) ¶ -
Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.
See Python’s logging module for details of message formatting.
-
param ObjectSelector
-
class
holoviews.operation.timeseries.
rolling_outlier_std
( **params ) [source] ¶ -
Bases:
holoviews.core.operation.Operation
,holoviews.operation.timeseries.RollingBase
Detect outliers using the standard deviation within a rolling window.
Outliers are the array elements outside sigma standard deviations from the smoothed trend line, as calculated from the trend line residuals.
The rolling window is controlled by parameters shared with the rolling operation via the base class RollingBase, to make it simpler to use the same settings for both.
-
param String
group
( allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False ) - The group string used to identify the output of the Operation. By default this should match the operation name.
-
param Integer
min_periods
( allow_None=True, bounds=None, constant=False, default=None, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, softbounds=None, time_dependent=False, time_fn=<Time Time00001> ) - Minimum number of observations in window required to have a value (otherwise result is NaN).
-
param Boolean
link_inputs
( allow_None=False, bounds=(0, 1), constant=False, default=False, instantiate=False, pickle_default_value=True, precedence=None, readonly=False ) - If the operation is dynamic, whether or not linked streams should be transferred from the operation inputs for backends that support linked streams. For example if an operation is applied to a DynamicMap with an RangeXY, this switch determines whether the corresponding visualization should update this stream with range changes originating from the newly generated axes.
-
param ObjectSelector
dynamic
( allow_None=None, check_on_set=True, compute_default_fn=None, constant=False, default=default, instantiate=False, objects=[‘default’, True, False], pickle_default_value=True, precedence=None, readonly=False ) - Whether the operation should be applied dynamically when a specific frame is requested, specified as a Boolean. If set to ‘default’ the mode will be determined based on the input type, i.e. if the data is a DynamicMap it will stay dynamic.
-
param Integer
rolling_window
( allow_None=False, bounds=None, constant=False, default=10, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, softbounds=None, time_dependent=False, time_fn=<Time Time00001> ) - The window size over which to operate.
-
param ClassSelector
input_ranges
( allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False ) - Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.
-
param List
streams
( allow_None=False, bounds=(0, None), constant=False, default=[], instantiate=True, pickle_default_value=True, precedence=None, readonly=False ) - List of streams that are applied if dynamic=True, allowing for dynamic interaction with the plot.
-
param Number
sigma
( allow_None=False, bounds=None, constant=False, default=2.0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, softbounds=None, time_dependent=False, time_fn=<Time Time00001> ) - Minimum sigma before a value is considered an outlier.
-
param Boolean
center
( allow_None=False, bounds=(0, 1), constant=False, default=True, instantiate=False, pickle_default_value=True, precedence=None, readonly=False ) - Whether to set the x-coordinate at the center or right edge of the window.
-
debug
( msg , *args , **kw ) ¶ -
Print msg merged with args as a debugging statement.
See Python’s logging module for details of message formatting.
-
defaults
( ) ¶ -
Return {parameter_name:parameter.default} for all non-constant Parameters.
Note that a Parameter for which instantiate==True has its default instantiated.
-
force_new_dynamic_value
= <functools.partial object> ¶
-
get_overlay_bounds
( overlay ) ¶ -
Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.
-
get_overlay_label
( overlay , default_label='' ) ¶ -
Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.
-
get_param_values
( onlychanged=False ) ¶ -
Return a list of name,value pairs for all Parameters of this object.
If onlychanged is True, will only return values that are not equal to the default value.
-
get_value_generator
= <functools.partial object> ¶
-
inspect_value
= <functools.partial object> ¶
-
instance
= <functools.partial object> ¶
-
message
( msg , *args , **kw ) ¶ -
Print msg merged with args as a message.
See Python’s logging module for details of message formatting.
-
params
( parameter_name=None ) ¶ -
Return the Parameters of this class as the dictionary {name: parameter_object}
Includes Parameters from this class and its superclasses.
-
pprint
( imports=None , prefix='\n ' , unknown_value='<?>' , qualify=False , separator='' ) ¶ -
Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y
-
print_param_defaults
( ) ¶ -
Print the default values of all cls’s Parameters.
-
print_param_values
( ) ¶ -
Print the values of all this object’s Parameters.
-
process_element
( element , key , **params ) ¶ -
The process_element method allows a single element to be operated on given an externally supplied key.
-
script_repr
( imports=[] , prefix=' ' ) ¶ -
Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y
-
search
( element , pattern ) ¶ -
Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.
The input may be a Layout, an Overlay type or a single Element.
-
set_default
( param_name , value ) ¶ -
Set the default value of param_name.
Equivalent to setting param_name on the class.
-
set_dynamic_time_fn
= <functools.partial object> ¶
-
set_param
= <functools.partial object> ¶
-
state_pop
( ) ¶ -
Restore the most recently saved state.
See state_push() for more details.
-
state_push
( ) ¶ -
Save this instance’s state.
For Parameterized instances, this includes the state of dynamically generated values.
Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().
Generally, this method is used by operations that need to test something without permanently altering the objects’ state.
-
verbose
( msg , *args , **kw ) ¶ -
Print msg merged with args as a verbose message.
See Python’s logging module for details of message formatting.
-
warning
( msg , *args , **kw ) ¶ -
Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.
See Python’s logging module for details of message formatting.
-
param String


Class: Aws::AutoScaling::Instance
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
|
|
|
Instance Chain:
|
|
| Inherits: |
Aws::Resources::Resource
|
| Defined in: | (unknown) |
Class Attribute Summary
::Aws::Resources::Resource - Inherited
Class Method Summary
- .new(group_name, id, options = {}) constructor
::Aws::Resources::Resource - Inherited
| .add_data_attribute | Registers a data attribute. |
| .add_identifier, .data_attributes, .identifiers, .new |
::Aws::Resources::OperationMethods - Extended
Instance Attribute Summary
-
#auto_scaling_group_name ⇒ String
readonly
The name of the Auto Scaling group associated with the instance.
. -
#availability_zone ⇒ String
readonly
The Availability Zone for the instance.
. - #group_name ⇒ String readonly
-
#health_status ⇒ String
readonly
The last reported health status of this instance.
- #id ⇒ String readonly
-
#instance_id ⇒ String
readonly
The ID of the instance.
. -
#launch_configuration_name ⇒ String
readonly
The launch configuration associated with the instance.
. -
#lifecycle_state ⇒ String
readonly
The lifecycle state for the instance.
-
#protected_from_scale_in ⇒ Boolean
readonly
Indicates whether the instance is protected from termination by Auto Scaling when scaling in.
.
::Aws::Resources::Resource - Inherited
| #client | Marked private to prevent double documentation. |
| #data_loaded?, | |
| #identifiers | Marked private to prevent double documentation. |
Instance Method Summary
-
#attach ⇒ Struct
Attaches one or more EC2 instances to the specified Auto Scaling group.
. - #detach(options = {}) ⇒ Array<Activity>
- #enter_standby(options = {}) ⇒ Array<Activity>
- #exit_standby ⇒ Array<Activity>
- #group ⇒ AutoScalingGroup
- #launch_configuration ⇒ LaunchConfiguration?
-
#set_health(options = {}) ⇒ Struct
Sets the health status of the specified instance.
. - #terminate(options = {}) ⇒ Activity
::Aws::Resources::Resource - Inherited
| #data, | |
| #load | Loads data for this resource. |
| #reload | Alias for Resources::Resource#load. |
| #wait_until | Waiter polls an API operation until a resource enters a desired state. |
Constructor Details
.new(group_name, id, options = {})
.new(options = {})
Instance Attribute Details
#auto_scaling_group_name ⇒ String
(readonly)
The name of the Auto Scaling group associated with the instance.
#availability_zone ⇒ String
(readonly)
The Availability Zone for the instance.
#group_name ⇒ String
(readonly)
#health_status ⇒ String
(readonly)
The last reported health status of this instance. "Healthy" means that the instance is healthy and should remain in service. "Unhealthy" means that the instance is unhealthy and Auto Scaling should terminate and replace it.
#id ⇒ String
(readonly)
#instance_id ⇒ String
(readonly)
The ID of the instance.
#launch_configuration_name ⇒ String
(readonly)
The launch configuration associated with the instance.
#lifecycle_state ⇒ String
(readonly)
The lifecycle state for the instance. For more information, see Auto Scaling Lifecycle in the Auto Scaling User Guide.
#protected_from_scale_in ⇒ Boolean
(readonly)
Indicates whether the instance is protected from termination by Auto Scaling when scaling in.
Instance Method Details
#attach ⇒ Struct
Attaches one or more EC2 instances to the specified Auto Scaling group.
When you attach instances, Auto Scaling increases the desired capacity of the group by the number of instances being attached. If the number of instances being attached plus the desired capacity of the group exceeds the maximum size of the group, the operation fails.
If there is a Classic load balancer attached to your Auto Scaling group, the instances are also registered with the load balancer. If there are target groups attached to your Auto Scaling group, the instances are also registered with the target groups.
For more information, see Attach EC2 Instances to Your Auto Scaling Group in the Auto Scaling User Guide.
#detach(options = {}) ⇒ Array
<Activity>
#enter_standby(options = {}) ⇒ Array
<Activity>
#exit_standby ⇒ Array
<Activity>
#group ⇒ AutoScalingGroup
#launch_configuration ⇒ LaunchConfiguration?
#set_health(options = {}) ⇒ Struct
Sets the health status of the specified instance.
For more information, see Health Checks in the Auto Scaling User Guide.


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Volume = increases the amount of clouds you can have at a time
VpS = increases how many clouds come in per second
Max SpV = increases the amount of silver you harvest per cloud
Licensed under the MIT License


The unprotected submission service is located here ...
The protected submission service is located here ...


We bring the best out of your shower: stone, tile, niche, and bench; we can create a look that is unique and will meet your needs. We also offer Schulter Systems shower components.


Gypsy's Tale is a small third-person adventure game inspired by First Tree, developed using Unity and written in C#. Take control of an inquisitive, cheeky Golden Retriever as he causes mischief in an inner-city park interacting with families, finding bones and making friends.
Gypsy's Tale was a semester-long project developed using Unity and C# as a part of my studies at the Academy of Interactive Entertainment (AIE). The project was developed by 7 games development students and was the final assessment task of the second year of studies.
For the Gypsy's Tale project, I was chosen as lead and sole programmer, the first time experiencing this kind of responsibility for a project of this size as well as in a group. One programmer was chosen for this project because the original scope and idea were much lower and simpler than ended up being produced, more on that below.
The overall goal for Gypsy's Tale was to create a simple, relaxing and quick pick up walking simulation game. The idea was to play as a dog and do various activities around an inner-city park, keeping the gameplay light and focusing on the art. I think for the most part we achieved this goal, with the exception of adding much more gameplay than was originally pitched and intended, resulting in polish issues towards the end of development.
During the development process of Gypsy's Tale, many things went wrong, many things out of my control, but also a few that I could have personally prevented. One of these issues being my ability to work as a sole programmer, it was an incredibly intimidating, stressful and overwhelming job for a project of this scale. I thought I could handle everything thrown at me, but I was wrong. I'm quite hard on myself about how this project ended up, but overall I think I did the best job I could and now know when to ask for help.
Other lessons learnt during the development of this project is how quickly a team can fall apart once a few members stop showing up for work. We were completely within the scope, development was on schedule, and team morale was high. Once development started ramping up, however, we lost four members (over half our team) and sadly resulting in everything slowly failing surrounding development. Quickly we scoped many elements of the project down to account for missing members, but a couple of objectives and the player controller suffered greatly as a part of this issue.
Gypsy's Tale is a small third-person adventure game inspired by First Tree, developed using Unity and written in C#. Take control of an inquisitive, cheeky Golden Retriever as he causes mischief in an inner-city park interacting with families, finding bones and making friends.


A block of rooms is available. Be sure to book yours by June 3rd! Great accommodations, moderately priced, and friendly staff. Just a hop, skip, and a jump from the wedding site.
3131 Campus Dr,
Plymouth, MN 55441
Transportation will be provided between the Crowne Plaza Hotel and the ceremony/reception venue. The shuttle will run: 4:00pm-6:00pm and 10:00pm-midnight.
Everyone loves brunch on Sunday morning after a wedding! Join the bride and groom at the Creekside Cafe, located on-site at the Crowne Plaza hotel at 9:00am.
Located in downtown Minneapolis. The bridge spans across the Mississippi river, connecting historic St. Anthony Main with the Guthrie Theatre and Mill City Museum. This is where the couple got engaged!
Features contemporary visual and performing arts. Located next to the Minneapolis Sculpture Garden (you know, the big spoon and cherry)!
1750 Hennepin Avenue,
Minneapolis, MN 55403
Located on the south side of Minneapolis, this is a fantastic park to take in some fresh air. If you get hungry, stop by the Sea Salt cafe just steps away from a beautiful view of the falls.
4801 South Minnehaha Drive,
Minneapolis, MN 55417
A favorite of the grooms family. Close proximity to the Crowne Plaza hotel. Features a beautiful patio and brings the best of the downtown dining experience to the suburbs.
16605 County Rd 24,
Plymouth, MN 55447
Succulent steak upstairs & wood fired pizza downstairs. The couple loves stopping in here to enjoy a Calabrese pizza.
1933 Colfax Ave S,
Minneapolis, MN 55403
A favorite of the brides family. Authentic German restaurant located in Northeast Minneapolis. Great place for an ice cold German beir and delicious shared platters.
2300 University Ave NE,
Minneapolis, MN 55418


GOP Sen. Lee on Impeachment: 'House Has Shown Only Unfairness -- Fairness to the President Vindicates the President' | Breitbart
by Jeff PoorTuesday on Fox News Channel’s “Fox News @ Night,” Sen. Mike Lee (R-UT) argued if House Democrats had not been fair to President Donald Trump in their so-called impeachment inquiry and if they had done so, fairness would have vindicated Trump.
The Utah Republican also praised Trump for getting Ukrainian officials to investigate Burisma.
“[W]hat I can say is that we’re going to show fairness where the House has shown only unfairness,” he said. “And fairness to the President vindicates the President. Now, what we’ve seen is that all the evidence that we’re aware of in this case shows that the President hasn’t done anything impeachable. He hasn’t done anything criminal. He’s not even done anything wrong.”
“President Trump succeeded where President Obama tried and failed, for years, to get the Ukrainian government to investigate Burisma,” Lee added. “That’s not impeachment material. There’s — that’s something to applaud.”
Follow Jeff Poor on Twitter @jeff_poor


Reporting
Reporting package contains BaseTableBuilder is the Builder design pattern.
Dependency
This package can depend on ImageJ


I noticed you did not have any experiences on hydromorphone, and since this is the first time I have done this particular drug, I would submit a report.
I obtained a 4mg pill of hydromorphone from an undisclosed location, and chopped it up into a fine powder.
I snorted 2mg of the substance. No burn.
I wait 10 min.. And feel I am breathing slower.. And my hands are clammy and there is a sense of being wanted, and I just nod and take in everything around me as being good.
I tell people exactly what I am thinking, and am generally pleased so far. So I decide to insufflate the last 2mg.
I am in a NOD now.. It feels so good to sit and enjoy the moment, it is not close to oxycontin feelings or hydrocodone, I would relate this high to heroin, to the best of my knowledge, and everything is A ok in my book.
This high is incredible, and I am slightly itching my arms, and the back of my kneecaps itch a little bit.
Iíve noticed there is a large amount of clamminess feeling to this drug, I prefer an oxycontin high to this, I'm more stimulated on that and europhic beyond pleasure.. This is bliss indeed, but in a whole different way.
I would recommend any opiate lover to try this once, or twice. But I can see strong reason why it is so addictive.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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As we got further and further away, it diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.
Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next 10. We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win.
As we got further and further away, it diminished in size. Finally it shrank to the size of a marble, the most beautiful you can imagine. That beautiful, warm, living object looked so fragile, so delicate, that if you touched it with a finger it would crumble and fall apart. Seeing this has to change a man.
Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next 10. We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win.
The path of a cosmonaut is not an easy, triumphant march to glory. You have to get to know the meaning not just of joy but also of grief, before
Content Providers
- Audiovisual Technologies Informatics
- British Universities Film and Video Council
- Nederlands Instituut voor Beeld en Geluid
- Deutsche Welle
- Istituto Luce Cinecittà
- Eötvös Loránd University
- Rádio e Televisão de Portugal
- Audiovisual Technologies Informatics
- British Universities Film and Video Council
- Nederlands Instituut voor Beeld en Geluid
- Deutsche Welle
- Istituto Luce Cinecittà
- Eötvös Loránd University
- Rádio e Televisão de Portugal
- Audiovisual Technologies Informatics
- British Universities Film and Video Council
- Nederlands Instituut voor Beeld en Geluid
- Deutsche Welle
- Istituto Luce Cinecittà
- Eötvös Loránd University
- Rádio e Televisão de Portugal


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All XRPs and IOUs are stored on the Ripple Network and not your device. InstantRipple.com does not have access to your username or password. If you lose your information you will lose your XRPs and IOUs.
Payments are irreversible. If you send XRPs or IOUs to an unknown party consider it a permanent transaction.
Keep your mobile device safe. Only install apps you fully trust. Malicious apps can steal your information and have full access to your device.
Reduce your risk by having two wallets. One wallet for daily usage and other as a savings account. Only transfer small amounts into your daily usage wallet as needed.


Bruce Wayne
Gotham, United Kingdom
<EMAIL_ADDRESS>www.bootsnipp.com
January 30, 1974
Bruce Wayne
Gotham, United Kingdom
<EMAIL_ADDRESS>www.bootsnipp.com
January 30, 1974