Belt test results – Success and disappointment « Doing It Wrong

Belt test results – Success and disappointment

by James Martini

Categories: Taekwondo

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Published on: June 11, 2009

So I did advance to senior green but my average score wasn’t as high as previous tests and I got my first “Needs practice”.

Boxing & Pad work

Intensity : Good
Punching from Guard: Needs Practice (I’m dropping my hands)
Hip & Shoulder Twist: Excellent
Stance: Excellent
Flow: Excellent

Sparring

Control: Outstanding
Combinations: Excellent (Combos are two or three moves, try to extend to four and five if possible)
Set Balance: Outstanding
Overall Confidence: Outstanding

Forms and Basics

Stances: Excellent
Hips: Excellent
Reaction Force: Outstanding
Intensity: Good
Sine Wave: Excellent
Focus: Excellent

I’m still on Song Song and Yong Gom forms so I can take the next 90 days and really focus on my pad work and keeping my hands up as well as go through both forms and make sure that I’m not getting sloppy with my stances and I’m really dropping into the moves and getting that extra snap into them. Fortunately, Mr Price and the sparring class is really helping with learning to keep my hands up, I dropped them a few times last night when I was tired and almost got my head knocked off.

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from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# load the data into training and test sets
iris = datasets.load_iris()
X, y = iris.data[:, :2], iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=13)

# scale the data
scaler = preprocessing.StandardScaler()
scaler.fit(X_train)
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knn.fit(X_train, y_train)

# Test the model
y_pred = knn.predict(X_test)
print(accuracy_score(y_test, y_pred))

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from sklearn import neighbors, datasets, preprocessing

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# load the data into training and test sets

iris = datasets.load_iris()

X, y = iris.data[:, :2], iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=13)

# scale the data

scaler = preprocessing.StandardScaler()

scaler.fit(X_train)

X_train = scaler.transform(X_train)

X_test = scaler.transform(X_test)

# train a k-nearest neighbors model

knn = neighbors.KNeighborsClassifier(n_neighbors = 5)

knn.fit(X_train, y_train)

# Test the model

y_pred = knn.predict(X_test)

print(accuracy_score(y_test, y_pred))

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$ grep scikit-learn handson-ml/requirements.txt scikit-learn==0.19.1 $ pip freeze |grep scikit-learn scikit-learn==0.19.2

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$ grep scikit-learn handson-ml/requirements.txt
scikit-learn==0.19.1
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scikit-learn==0.19.2

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