 garden. It was based on the Chinese legend of the red string of fate. The myth states that everyone is born with a red string tied round their ankle. This links them to those who they are destined to encounter in their lifetime. Robert had woven a complex pattern of red strings round oak posts in the garden to illustrate this idea. Visitors were also handed some red string and were invited to interact and add this to the posts. This added their connection to the garden.
This garden made me think about the complex patterns of the relationships we form through our life time and how these are often based on what initially seems like a chance encounter. Talking of connections, I found out later through Twitter that I had also worked with one of the volunteers working on the garden when we were at the RHS Chelsea Flower Show a few years ago. I thought this conceptual garden got a well deserved gold medal, which was fantastic as it was Robert Barker’s first RHS show garden.
The Wildfowl and Wetlands Trust built this Hampton Court garden to increase awareness of the effect that water running off your roof, patio, decking and driveways has during heavy rainfall. When rain runs off multiple properties in your area, all this surface water has nowhere to go. It can overwhelm drains and cause local flooding. This garden stresses the importance of reducing the amount of impermeable hard surfaces in a garden. We are much better to use alternative permeable materials such as gravel, permeable paving and areas of planting. These provide somewhere for the water to go. This garden channels the water from the roof through gravel beds, flower beds and soakaway hollows. The water flows through an attractive water feature and spills in to a wildlife pond.
Jeni Cairns cleverly designed this garden. She was creative in using recycled industrial materials very effectively as innovative landscaping materials. For example, she created the pagoda roof from the roof of an inverted grain silo. The show garden will also be recycled. The WWT will transport it to their Washington Wetland Centre, where it will become an outdoor classroom.
This is an idea that we can all adapt for our own gardens. We can create more permeable surfaces and channel water into flower beds and ponds or soakaway hollows. We can also build green roofs, which will also act as a holding bay for water during heavy rainfall. As well as reducing flooding, you will be bringing your garden to life by enticing wildlife in to your garden. Furthermore, we can find some fascinating one off pieces by rummaging through salvage yards. With a bit of creativity, you can recycle these to make your garden a truly individual and less wasteful space. And above all else you will be creating beautiful place.
Finally, as we were leaving the RHS Hampton Court Flower Show we visited the Bowel Disease UK Garden. This was designed by Fisher Tomlin and Bowyer. This garden had a ‘colon’ shaped path winding through lush architectural planting. The abundant green planting scheme of tree ferns, grasses, hostas, ferns, brunnera, etc gave a real feeling of sanctuary. Again, you could adapt this idea at home to create your own urban retreat in even the smallest of spaces. We weren’t sure about the two ‘stools’ stuck in the middle of the garden though!#############################################################################################################################################
# XG_Model.py
# Author(s): <NAME>
# Version 1.0
# Description: This is an attempt to create a logistic regression model for expected goals given statistics on NHL Shots. 
# Logisitic regression was chosen because logistic regression is effective when the dependent variable is a discrete(categorical) value.
# Due to a relatively small number of features, logistic regression is a computationally efficient algorithm to use.
# Dependencies:
#############################################################################################################################################

# Import Necessary Libraries
import pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt 
plt.rc("font", size=14)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, roc_curve
import seaborn as sns
import statsmodels.api as sm

# If there are warnings, I want to ignore them for now. 
import warnings
warnings.filterwarnings('ignore')

# Import and Format Data 
data = pd.read_csv('shots.csv')
data = data.dropna()

# Printed out data shape for analysis.
#Sprint(data.shape)

# Set features and target variables
x = data[['period', 'time', 'distance', 'angle', 'is_rebound', 'is_pass_before', 'is_cross_line', 'is_one_touch', 'is_fast_attack']]
y = data[['is_goal']]

# Print out means to take a closer look at the data.
#print(data['is_goal'].value_counts())
#print(data.groupby('is_goal').mean())

logit_model=sm.Logit(y.astype(float),x.astype(float))
result=logit_model.fit()
print(result.summary2())

# Curious to see if event type has affect on goals. It seems like Upper Left and Upper Right have the greatest occurences of a goal.
pd.crosstab(data.event, data.is_goal).plot(kind='bar')
plt.title('Goal Frequency for Event Type')
plt.xlabel('Event')
plt.ylabel('Frequency of Goal')
plt.show()

# Implement a logistic regression model.
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 0)
log_reg = LogisticRegression()
log_reg.fit(x_train, y_train)

y_predict = log_reg.predict(x_test)
print('Accuracy of Logistic Regression on Test Set: {:.2f}'.format(log_reg.score(x_test, y_test)))
confusion_matrix = confusion_matrix(y_test, y_predict)

print('The Confusion Matrix is:')
print(confusion_matrix)
print(classification_report(y_test, y_predict))

log_roc_auc = roc_auc_score(y_test, log_reg.predict(x_test))
fpr, tpr, thresholds = roc_curve(y_test, log_reg.predict_proba(x_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Logistic Regression')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc="lower right")
plt.savefig('Log_ROC')
plt.show()Budovich Lidia Sergeevna, Department of Economics and Innovative Entrepreneurship, Faculty of Economics and Law, MIREA – Russian Technological University (RTU MIREA), Moscow, Russian Federation.
Abstract: The article discusses the task of development of the Russian industry is not only to ensure the transition of the economy to the trajectory of sustainable development, but also to change the quality of economic growth. Here, an important role belongs to the state scientific and industrial policy which determines the overall economic strategy and the most promising basis for development. Experience shows that the priority development of the resource-based industries that have become the leading sectors of the Russian economy to date is unable to solve the problem of economic upsurge fundamentally and for the long haul. Creating new high-tech products is a complex and multifaceted process that includes scientific, technical, production, economic and social aspects, and the functioning in the market conditions requires improving the organization and management of production in the lower tier of the national economy, i.e. at the enterprise level. In the process of development of new products, certain production and management relations are established between the business units of the enterprise and between related firms, which relations reflect the links between the direct manufacturers of products, management staff, as well the organization of joint activities of participants in the production of new products. Economic growth should be based on the priority development of the group of export-oriented industries capable of being a stable, long-term driving force of development, the most promising basis for the growth of the Russian economy due to intensive factors. High-tech enterprises of the defense industry being part of the High-Tech Complex (HTC) of Russia basically form the backbone of such industries.
Keywords: Knowledge-Intensive Enterprises, Innovations.Facebook's Zuckerberg hits pause