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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object | +--org.easymock.MockControl
A MockControl
object controls the behavior of its associated
mock object. For more information, see the EasyMock documentation.
| Field Summary | |
static ArgumentsMatcher |
ALWAYS_MATCHER
Matches always. |
static ArgumentsMatcher |
ARRAY_MATCHER
Matches if each expected argument is equal to the corresponding actual argument for non-array arguments; array arguments are compared with the appropriate java.util.Arrays.equals() -method. |
static ArgumentsMatcher |
EQUALS_MATCHER
Matches if each expected argument is equal to the corresponding actual argument. |
protected static org.easymock.internal.IBehaviorFactory |
NICE_BEHAVIOR_FACTORY
internal constant with protected
visibility to allow access for extensions. |
static org.easymock.internal.Range |
ONE
Exactly one call. |
static org.easymock.internal.Range |
ONE_OR_MORE
One or more calls. |
protected static org.easymock.internal.IBehaviorFactory |
ORDERED_BEHAVIOR_FACTORY
internal constant with protected
visibility to allow access for extensions. |
protected static org.easymock.internal.IBehaviorFactory |
UNORDERED_BEHAVIOR_FACTORY
internal constant with protected
visibility to allow access for extensions. |
static org.easymock.internal.Range |
ZERO_OR_MORE
Zero or more calls. |
| Constructor Summary | |
protected |
MockControl(java.lang.Class toMock,
org.easymock.internal.IProxyFactory proxyFactory,
org.easymock.internal.IBehaviorFactory behaviorFactory)
Creates a new mock control object using the provided proxy and behavior factory - this is an internal constructor with protected
visibility to allow access for extensions. |
| Method Summary | |
static MockControl |
createControl(java.lang.Class toMock)
Creates a mock control object for the specified interface. |
static MockControl |
createNiceControl(java.lang.Class toMock)
Creates a mock control object for the specified interface. |
static MockControl |
createStrictControl(java.lang.Class toMock)
Creates a mock control object for the specified interface. |
void |
expectAndReturn(boolean ignored,
boolean value)
Same as setReturnValue(boolean) . |
void |
expectAndReturn(boolean ignored,
boolean value,
int count)
Same as setReturnValue(boolean, int) . |
void |
expectAndReturn(boolean ignored,
boolean value,
int min,
int max)
Same as setReturnValue(boolean, int, int) . |
void |
expectAndReturn(boolean ignored,
boolean value,
org.easymock.internal.Range range)
Same as setReturnValue(boolean, Range) . |
void |
expectAndReturn(double ignored,
double value)
Same as setReturnValue(double) . |
void |
expectAndReturn(double ignored,
double value,
int count)
Same as setReturnValue(double, int) . |
void |
expectAndReturn(double ignored,
double value,
int min,
int max)
Same as setReturnValue(double, int, int) . |
void |
expectAndReturn(double ignored,
double value,
org.easymock.internal.Range range)
Same as setReturnValue(double, Range) . |
void |
expectAndReturn(float ignored,
float value)
Same as setReturnValue(float) . |
void |
expectAndReturn(float ignored,
float value,
int count)
Same as setReturnValue(float, int) . |
void |
expectAndReturn(float ignored,
float value,
int min,
int max)
Same as setReturnValue(float, int, int) . |
void |
expectAndReturn(float ignored,
float value,
org.easymock.internal.Range range)
Same as setReturnValue(float, Range) . |
void |
expectAndReturn(long ignored,
long value)
Same as setReturnValue(long) . |
void |
expectAndReturn(long ignored,
long value,
int count)
Same as setReturnValue(long, int) . |
void |
expectAndReturn(long ignored,
long value,
int min,
int max)
Same as setReturnValue(long, int, int) . |
void |
expectAndReturn(long ignored,
long value,
org.easymock.internal.Range range)
Same as setReturnValue(long, Range) . |
void |
expectAndReturn(java.lang.Object ignored,
java.lang.Object value)
Same as setReturnValue(Object) . |
void |
expectAndReturn(java.lang.Object ignored,
java.lang.Object value,
int count)
Same as setReturnValue(Object, int) . |
void |
expectAndReturn(java.lang.Object ignored,
java.lang.Object value,
int min,
int max)
Same as setReturnValue(Object, int, int) . |
void |
expectAndReturn(java.lang.Object ignored,
java.lang.Object value,
org.easymock.internal.Range range)
Same as setReturnValue(Object, Range) . |
void |
expectAndThrow(boolean ignored,
java.lang.Throwable throwable)
Same as setThrowable(Throwable) . |
void |
expectAndThrow(boolean ignored,
java.lang.Throwable throwable,
int count)
Same as setThrowable(Throwable, int) . |
void |
expectAndThrow(boolean ignored,
java.lang.Throwable throwable,
int min,
int max)
Same as setThrowable(Throwable, int, int) . |
void |
expectAndThrow(boolean ignored,
java.lang.Throwable throwable,
org.easymock.internal.Range range)
Same as setThrowable(Throwable, Range) . |
void |
expectAndThrow(double ignored,
java.lang.Throwable throwable)
Same as setThrowable(Throwable) . |
void |
expectAndThrow(double ignored,
java.lang.Throwable throwable,
int count)
Same as setThrowable(Throwable, int) . |
void |
expectAndThrow(double ignored,
java.lang.Throwable throwable,
int min,
int max)
Same as setThrowable(Throwable, int, int) . |
void |
expectAndThrow(double ignored,
java.lang.Throwable throwable,
org.easymock.internal.Range range)
Same as setThrowable(Throwable, Range) . |
void |
expectAndThrow(float ignored,
java.lang.Throwable throwable)
Same as setThrowable(Throwable) . |
void |
expectAndThrow(float ignored,
java.lang.Throwable throwable,
int count)
Same as setThrowable(Throwable, int) . |
void |
expectAndThrow(float ignored,
java.lang.Throwable throwable,
int min,
int max)
Same as setThrowable(Throwable, int, int) . |
void |
expectAndThrow(float ignored,
java.lang.Throwable throwable,
org.easymock.internal.Range range)
Same as setThrowable(Throwable, Range) . |
void |
expectAndThrow(long ignored,
java.lang.Throwable throwable)
Same as setThrowable(Throwable) . |
void |
expectAndThrow(long ignored,
java.lang.Throwable throwable,
int count)
Same as setThrowable(Throwable, int) . |
void |
expectAndThrow(long ignored,
java.lang.Throwable throwable,
int min,
int max)
Same as setThrowable(Throwable, int, int) . |
void |
expectAndThrow(long ignored,
java.lang.Throwable throwable,
org.easymock.internal.Range range)
Same as setThrowable(Throwable, Range) . |
void |
expectAndThrow(java.lang.Object ignored,
java.lang.Throwable throwable)
Same as setThrowable(Throwable) . |
void |
expectAndThrow(java.lang.Object ignored,
java.lang.Throwable throwable,
int count)
Same as setThrowable(Throwable, int) . |
void |
expectAndThrow(java.lang.Object ignored,
java.lang.Throwable throwable,
int min,
int max)
Same as setThrowable(Throwable, int, int) . |
void |
expectAndThrow(java.lang.Object ignored,
java.lang.Throwable throwable,
org.easymock.internal.Range range)
Same as setThrowable(Throwable, Range) . |
java.lang.Object |
getMock()
Returns the mock object. |
void |
replay()
Switches the mock object from record state to replay state. |
void |
reset()
Resets the mock control and the mock object to the state directly after creation. |
void |
setDefaultMatcher(ArgumentsMatcher matcher)
Sets the default ArgumentsMatcher for all methods of the mock object. |
void |
setDefaultReturnValue(boolean value)
Records that the mock object will by default allow the last method specified by a method call, and will react by returning the provided return value. |
void |
setDefaultReturnValue(double value)
Records that the mock object will by default allow the last method specified by a method call, and will react by returning the provided return value. |
void |
setDefaultReturnValue(float value)
Records that the mock object will by default allow the last method specified by a method call, and will react by returning the provided return value. |
void |
setDefaultReturnValue(long value)
Records that the mock object will by default allow the last method specified by a method call, and will react by returning the provided return value. |
void |
setDefaultReturnValue(java.lang.Object value)
Records that the mock object will by default allow the last method specified by a method call, and will react by returning the provided return value. |
void |
setDefaultThrowable(java.lang.Throwable throwable)
Records that the mock object will by default allow the last method specified by a method call, and will react by throwing the provided Throwable. |
void |
setDefaultVoidCallable()
Records that the mock object will by default allow the last method specified by a method call. |
void |
setMatcher(ArgumentsMatcher matcher)
Sets the ArgumentsMatcher for the last method called on the mock object. |
void |
setReturnValue(boolean value)
Records that the mock object will expect the last method call once, and will react by returning the provided return value. |
void |
setReturnValue(boolean value,
int times)
Records that the mock object will expect the last method call a fixed number of times, and will react by returning the provided return value. |
void |
setReturnValue(boolean value,
int minCount,
int maxCount)
Records that the mock object will expect the last method call between minCount and maxCount times, and will
react by returning the provided return value. |
void |
setReturnValue(boolean value,
org.easymock.internal.Range range)
Records that the mock object will expect the last method call the number of times specified by the range argument, and will react by returning the provided return value. |
void |
setReturnValue(double value)
Records that the mock object will expect the last method call once, and will react by returning the provided return value. |
void |
setReturnValue(double value,
int times)
Records that the mock object will expect the last method call a fixed number of times, and will react by returning the provided return value. |
void |
setReturnValue(double value,
int minCount,
int maxCount)
Records that the mock object will expect the last method call between minCount and maxCount times, and will
react by returning the provided return value. |
void |
setReturnValue(double value,
org.easymock.internal.Range range)
Records that the mock object will expect the last method call the number of times specified by the range argument, and will react by returning the provided return value. |
void |
setReturnValue(float value)
Records that the mock object will expect the last method call once, and will react by returning the provided return value. |
void |
setReturnValue(float value,
int times)
Records that the mock object will expect the last method call a fixed number of times, and will react by returning the provided return value. |
void |
setReturnValue(float value,
int minCount,
int maxCount)
Records that the mock object will expect the last method call between minCount and maxCount times, and will
react by returning the provided return value. |
void |
setReturnValue(float value,
org.easymock.internal.Range range)
Records that the mock object will expect the last method call the number of times specified by the range argument, and will react by returning the provided return value. |
void |
setReturnValue(long value)
Records that the mock object will expect the last method call once, and will react by returning the provided return value. |
void |
setReturnValue(long value,
int times)
Records that the mock object will expect the last method call a fixed number of times, and will react by returning the provided return value. |
void |
setReturnValue(long value,
int minCount,
int maxCount)
Records that the mock object will expect the last method call between minCount and maxCount times, and will
react by returning the provided return value. |
void |
setReturnValue(long value,
org.easymock.internal.Range range)
Records that the mock object will expect the last method call the number of times specified by the range argument, and will react by returning the provided return value. |
void |
setReturnValue(java.lang.Object value)
Records that the mock object will expect the last method call once, and will react by returning the provided return value. |
void |
setReturnValue(java.lang.Object value,
int times)
Records that the mock object will expect the last method call a fixed number of times, and will react by returning the provided return value. |
void |
setReturnValue(java.lang.Object value,
int minCount,
int maxCount)
Records that the mock object will expect the last method call between minCount and maxCount times, and will
react by returning the provided return value. |
void |
setReturnValue(java.lang.Object object,
org.easymock.internal.Range range)
Records that the mock object will expect the last method call the number of times specified by the range argument, and will react by returning the provided return value. |
void |
setThrowable(java.lang.Throwable throwable)
Records that the mock object will expect the last method call once, and will react by throwing the provided Throwable. |
void |
setThrowable(java.lang.Throwable throwable,
int times)
Records that the mock object will expect the last method call a fixed number of times, and will react by throwing the provided Throwable. |
void |
setThrowable(java.lang.Throwable throwable,
int minCount,
int maxCount)
Records that the mock object will expect the last method call between minCount and maxCount times, and will
react by throwing the provided Throwable. |
void |
setThrowable(java.lang.Throwable throwable,
org.easymock.internal.Range range)
Records that the mock object will expect the last method call the number of times specified by the range argument, and will react by throwing the provided Throwable. |
void |
setVoidCallable()
Records that the mock object will expect the last method call once, and will react by returning silently. |
void |
setVoidCallable(int times)
Records that the mock object will expect the last method call a fixed number of times, and will react by returning silently. |
void |
setVoidCallable(int minCount,
int maxCount)
Records that the mock object will expect the last method call between minCount and maxCount times, and will
react by returning silently. |
void |
setVoidCallable(org.easymock.internal.Range range)
Records that the mock object will expect the last method call the number of times specified by the range argument, and will react by returning silently. |
void |
verify()
Verifies that all expectations have been met. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
protected static final org.easymock.internal.IBehaviorFactory NICE_BEHAVIOR_FACTORY
protected
visibility to allow access for extensions.
protected static final org.easymock.internal.IBehaviorFactory ORDERED_BEHAVIOR_FACTORY
protected
visibility to allow access for extensions.
protected static final org.easymock.internal.IBehaviorFactory UNORDERED_BEHAVIOR_FACTORY
protected
visibility to allow access for extensions.
public static final org.easymock.internal.Range ONE
public static final org.easymock.internal.Range ONE_OR_MORE
public static final org.easymock.internal.Range ZERO_OR_MORE
public static final ArgumentsMatcher EQUALS_MATCHER
public static final ArgumentsMatcher ALWAYS_MATCHER
public static final ArgumentsMatcher ARRAY_MATCHER
java.util.Arrays.equals()
-method.
| Constructor Detail |
protected MockControl(java.lang.Class toMock, org.easymock.internal.IProxyFactory proxyFactory, org.easymock.internal.IBehaviorFactory behaviorFactory)
protected
visibility to allow access for extensions.
toMock
- the class of the interface to mock.proxyFactory
- the proxy factory.behaviorFactory
- the behavior factory.| Method Detail |
public static MockControl createControl(java.lang.Class toMock)
MockControl
and its associated mock object will not check the
order of expected method calls. An unexpected method call on the mock
object will lead to an AssertionFailedError
.
toMock
- the class of the interface to mock.
public static MockControl createStrictControl(java.lang.Class toMock)
MockControl
and its associated mock object will check the order
of expected method calls. An unexpected method call on the mock object
will lead to an AssertionFailedError
.
toMock
- the class of the interface to mock.
public static MockControl createNiceControl(java.lang.Class toMock)
MockControl
and its associated mock object will not the order of
expected method calls. An unexpected method call on the mock object will
return an empty value (0, null, false).
toMock
- the class of the interface to mock.
public java.lang.Object getMock()
public final void reset()
public void replay()
java.lang.IllegalStateException
- if the mock object already is in replay state.public void verify()
java.lang.IllegalStateException
- if the mock object is in record state.
AssertionFailedError
- if any expectation has not been met.public void setVoidCallable()
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before, or if the last method
called on the mock was no void method.public void setThrowable(java.lang.Throwable throwable)
throwable
- the Throwable to throw.
java.lang.IllegalStateException
- if the mock object is in replay state or if no method was
called on the mock object before.
java.lang.IllegalArgumentException
- if the last method called on the mock cannot throw the
provided Throwable.
java.lang.NullPointerException
- if throwable is null.public void setReturnValue(boolean value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return boolean
.public void setReturnValue(long value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return byte, short, char, int, or long
.public void setReturnValue(float value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return float
.public void setReturnValue(double value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return double
.public void setReturnValue(java.lang.Object value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return an object.
java.lang.IllegalArgumentException
- if the provided return value is not compatible to the return
value of the last method called on the mock object.public void setVoidCallable(int times)
times
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before, or if the last method
called on the mock was no void method.public void setThrowable(java.lang.Throwable throwable, int times)
throwable
- the Throwable to throw.times
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state or if no method was
called on the mock object before.
java.lang.IllegalArgumentException
- if the last method called on the mock cannot throw the
provided Throwable.
java.lang.NullPointerException
- if throwable is null.public void setReturnValue(boolean value, int times)
value
- the return value.times
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return boolean
.public void setReturnValue(double value, int times)
value
- the return value.times
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return double
.public void setReturnValue(float value, int times)
value
- the return value.times
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return float
.public void setReturnValue(long value, int times)
value
- the return value.times
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return byte, short, char, int, or long
.public void setReturnValue(java.lang.Object value, int times)
value
- the return value.times
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return an object.
java.lang.IllegalArgumentException
- if the provided return value is not compatible to the return
value of the last method called on the mock object.public void setVoidCallable(org.easymock.internal.Range range)
ZERO_OR_MORE
,
ONE
and ONE_OR_MORE
.
range
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before, or if the last method
called on the mock was no void method.public void setThrowable(java.lang.Throwable throwable, org.easymock.internal.Range range)
ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
throwable
- the Throwable to throw.range
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state or if no method was
called on the mock object before.
java.lang.IllegalArgumentException
- if the last method called on the mock cannot throw the
provided Throwable.
java.lang.NullPointerException
- if throwable is null.public void setReturnValue(boolean value, org.easymock.internal.Range range)
ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.range
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return boolean
.public void setReturnValue(double value, org.easymock.internal.Range range)
ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.range
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return double
.public void setReturnValue(float value, org.easymock.internal.Range range)
ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.range
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return float
.public void setReturnValue(long value, org.easymock.internal.Range range)
ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.range
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return byte, short, char, int, or long
.public void setReturnValue(java.lang.Object object, org.easymock.internal.Range range)
ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
range
- the number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return an object.
java.lang.IllegalArgumentException
- if the provided return value is not compatible to the return
value of the last method called on the mock object.public void setDefaultVoidCallable()
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before, or if the last method
called on the mock was no void method.public void setDefaultThrowable(java.lang.Throwable throwable)
throwable
- throwable the throwable to be thrown
java.lang.IllegalArgumentException
- if the last method called on the mock cannot throw the
provided Throwable.
java.lang.NullPointerException
- if throwable is null.
java.lang.IllegalStateException
- if the mock object is in replay state, or if no method
was called on the mock object before.public void setDefaultReturnValue(boolean value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return boolean
.public void setDefaultReturnValue(long value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return byte, short, char, int, or long
.public void setDefaultReturnValue(float value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return float
.public void setDefaultReturnValue(double value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return double
.public void setDefaultReturnValue(java.lang.Object value)
value
- the return value.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return an object.
java.lang.IllegalArgumentException
- if the provided return value is not compatible to the return
value of the last method called on the mock object.public void setMatcher(ArgumentsMatcher matcher)
java.lang.IllegalStateException
- if called in replay state, or if no method was called on the
mock object before.public void setVoidCallable(int minCount, int maxCount)
minCount
and maxCount
times, and will
react by returning silently.
minCount
- the minimum number of times that the call is expected.maxCount
- the maximum number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before, or if the last method
called on the mock was no void method.public void setThrowable(java.lang.Throwable throwable, int minCount, int maxCount)
minCount
and maxCount
times, and will
react by throwing the provided Throwable. Available range arguments are
ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
throwable
- the Throwable to throw.minCount
- the minimum number of times that the call is expected.maxCount
- the maximum number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state or if no method was
called on the mock object before.
java.lang.IllegalArgumentException
- if the last method called on the mock cannot throw the
provided Throwable.
java.lang.NullPointerException
- if throwable is null.public void setReturnValue(boolean value, int minCount, int maxCount)
minCount
and maxCount
times, and will
react by returning the provided return value. Available range arguments
are ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.minCount
- the minimum number of times that the call is expected.maxCount
- the maximum number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return boolean
.public void setReturnValue(long value, int minCount, int maxCount)
minCount
and maxCount
times, and will
react by returning the provided return value. Available range arguments
are ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.minCount
- the minimum number of times that the call is expected.maxCount
- the maximum number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return byte, short, char, int, or long
.public void setReturnValue(float value, int minCount, int maxCount)
minCount
and maxCount
times, and will
react by returning the provided return value. Available range arguments
are ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.minCount
- the minimum number of times that the call is expected.maxCount
- the maximum number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return float
.public void setReturnValue(double value, int minCount, int maxCount)
minCount
and maxCount
times, and will
react by returning the provided return value. Available range arguments
are ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.minCount
- the minimum number of times that the call is expected.maxCount
- the maximum number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return double
.public void setReturnValue(java.lang.Object value, int minCount, int maxCount)
minCount
and maxCount
times, and will
react by returning the provided return value. Available range arguments
are ZERO_OR_MORE
,ONE
and
ONE_OR_MORE
.
value
- the return value.minCount
- the minimum number of times that the call is expected.maxCount
- the maximum number of times that the call is expected.
java.lang.IllegalStateException
- if the mock object is in replay state, if no method was
called on the mock object before. or if the last method
called on the mock does not return an object.
java.lang.IllegalArgumentException
- if the provided return value is not compatible to the return
value of the last method called on the mock object.public void setDefaultMatcher(ArgumentsMatcher matcher)
java.lang.IllegalStateException
- if called in replay state, or if any behavior is already
defined on the mock object.public void expectAndReturn(boolean ignored, boolean value)
setReturnValue(boolean)
. For explanation,
see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(long ignored, long value)
setReturnValue(long)
. For explanation, see
"Convenience Methods for Return Values" in the EasyMock documentation.
ignored
- an ignored value.public void expectAndReturn(float ignored, float value)
setReturnValue(float)
. For explanation, see
"Convenience Methods for Return Values" in the EasyMock documentation.
ignored
- an ignored value.public void expectAndReturn(double ignored, double value)
setReturnValue(double)
. For explanation,
see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(java.lang.Object ignored, java.lang.Object value)
setReturnValue(Object)
. For explanation,
see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(boolean ignored, boolean value, org.easymock.internal.Range range)
setReturnValue(boolean, Range)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(long ignored, long value, org.easymock.internal.Range range)
setReturnValue(long, Range)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(float ignored, float value, org.easymock.internal.Range range)
setReturnValue(float, Range)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(double ignored, double value, org.easymock.internal.Range range)
setReturnValue(double, Range)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(java.lang.Object ignored, java.lang.Object value, org.easymock.internal.Range range)
setReturnValue(Object, Range)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(boolean ignored, boolean value, int count)
setReturnValue(boolean, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(long ignored, long value, int count)
setReturnValue(long, int)
. For explanation,
see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(float ignored, float value, int count)
setReturnValue(float, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(double ignored, double value, int count)
setReturnValue(double, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(java.lang.Object ignored, java.lang.Object value, int count)
setReturnValue(Object, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(boolean ignored, boolean value, int min, int max)
setReturnValue(boolean, int, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(long ignored, long value, int min, int max)
setReturnValue(long, int, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(float ignored, float value, int min, int max)
setReturnValue(float, int, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(double ignored, double value, int min, int max)
setReturnValue(double, int, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndReturn(java.lang.Object ignored, java.lang.Object value, int min, int max)
setReturnValue(Object, int, int)
. For
explanation, see "Convenience Methods for Return Values" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(boolean ignored, java.lang.Throwable throwable)
setThrowable(Throwable)
. For explanation,
see "Convenience Methods for Throwables" in the EasyMock documentation.
ignored
- an ignored value.public void expectAndThrow(long ignored, java.lang.Throwable throwable)
setThrowable(Throwable)
. For explanation,
see "Convenience Methods for Throwables" in the EasyMock documentation.
ignored
- an ignored value.public void expectAndThrow(float ignored, java.lang.Throwable throwable)
setThrowable(Throwable)
. For explanation,
see "Convenience Methods for Throwables" in the EasyMock documentation.
ignored
- an ignored value.public void expectAndThrow(double ignored, java.lang.Throwable throwable)
setThrowable(Throwable)
. For explanation,
see "Convenience Methods for Throwables" in the EasyMock documentation.
ignored
- an ignored value.public void expectAndThrow(java.lang.Object ignored, java.lang.Throwable throwable)
setThrowable(Throwable)
. For explanation,
see "Convenience Methods for Throwables" in the EasyMock documentation.
ignored
- an ignored value.public void expectAndThrow(boolean ignored, java.lang.Throwable throwable, org.easymock.internal.Range range)
setThrowable(Throwable, Range)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(long ignored, java.lang.Throwable throwable, org.easymock.internal.Range range)
setThrowable(Throwable, Range)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(float ignored, java.lang.Throwable throwable, org.easymock.internal.Range range)
setThrowable(Throwable, Range)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(double ignored, java.lang.Throwable throwable, org.easymock.internal.Range range)
setThrowable(Throwable, Range)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(java.lang.Object ignored, java.lang.Throwable throwable, org.easymock.internal.Range range)
setThrowable(Throwable, Range)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(boolean ignored, java.lang.Throwable throwable, int count)
setThrowable(Throwable, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(long ignored, java.lang.Throwable throwable, int count)
setThrowable(Throwable, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(float ignored, java.lang.Throwable throwable, int count)
setThrowable(Throwable, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(double ignored, java.lang.Throwable throwable, int count)
setThrowable(Throwable, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(java.lang.Object ignored, java.lang.Throwable throwable, int count)
setThrowable(Throwable, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(boolean ignored, java.lang.Throwable throwable, int min, int max)
setThrowable(Throwable, int, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(long ignored, java.lang.Throwable throwable, int min, int max)
setThrowable(Throwable, int, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(float ignored, java.lang.Throwable throwable, int min, int max)
setThrowable(Throwable, int, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(double ignored, java.lang.Throwable throwable, int min, int max)
setThrowable(Throwable, int, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.public void expectAndThrow(java.lang.Object ignored, java.lang.Throwable throwable, int min, int max)
setThrowable(Throwable, int, int)
. For
explanation, see "Convenience Methods for Throwables" in the EasyMock
documentation.
ignored
- an ignored value.
|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


|
TFKaldi
|
an abstract class for a kaldi GMM More...
Public Member Functions | |
| def | __init__ (self, conf) |
| KaldiGMM constructor. More... | |
| def | train (self) |
| train the GMM | |
| def | align (self) |
| use the GMM to align the training utterances | |
| def | test (self) |
| test the GMM on the testing set | |
| def | name (self) |
| the name of the GMM | |
| def | trainscript (self) |
| the script used for training the GMM | |
| def | confFile (self) |
| the configuration file for this GMM | |
| def | parentGmmAlignments (self) |
| the path to the parent GMM model (empty for monophone GMM) | |
| def | trainops (self) |
| the extra options for GMM training | |
| def | graphopts (self) |
| the extra options for the decoding graph creation | |
Public Attributes | |
| conf | |
an abstract class for a kaldi GMM
| def kaldiGMM.KaldiGMM.__init__ | ( | self, | |
| conf | |||
| ) |
KaldiGMM constructor.
| conf | the general configurations |


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In Electron, this is the correct way to include jQuery<-->


Hi all,
I stumbled upon this project by chance when looking into plug'n'play temperature monitoring solutions (that are all rather expensive and inflexible) and I'm quite impressed by the work and potential.
Please allow for a rather beginner question if I am on the right track:
Planned:
- Monitor up to 8 different temperatures of my central heating with a sensor in an immersion sleeve
- Monitor up to 8 different room temperatures
- Potentially monitor output of a Combined Heat and Power unit
Selected Hardware:
- 1x Raspberry Pi with a RFM12Pi expansion board
- 1x emonTx 868Mhz kit
- 8x Encapsulated DS18B20 temperature sensor attached to the emonTx on a 1-wire bus
- 1x 100A max clip-on current sensor CT attached to the emonTx
- 8x emonTx Temperature Nodes 868Mhz for the room temperature
Does this make sense or am I on the wrong track?
Additionally can someone point me to the diameter of the encapsulated DS18B20 as my immersion sleeves will max fit 6mm.
Re: Temperature monitoring project
That looks generally OK to me. As the temperatures change only slowly, you shouldn't have a problem with collisions in the radio channel. However, you might want to read up on the temperature sensor to see whether the wiring constraints of the one-wire bus can fit in with your arrangements.
But you're out of luck with the temperature sensor size - the encapsulated one from the shop that I have is a tight 6.5 mm overall diameter. The body itself is exactly 6 mm, there's a heat-shrink sleeve over the back end that supports and waterproofs the cable, and that takes it up to 6.5 mm. Maybe as you don't need it to be sealed, you can use discrete DS18B20s, solder and sleeve the connections yourself? - the sensor IC itself is a TO-92, 5.5 mm diameter.
If you want logging, you may want to add a hard disk drive to your Raspberry Pi.
Re: Temperature monitoring project
Many thanks for this first feedback.
Basically I could create the sensors myself as there are 6mm empty metal sleeves available and together with a heat conductive casting compound it would just work nicely.
But as I am basically lazy, I'd prefer to have them ready made. Is there a chance to just cut the heat-shrink sleeve?
As for the sensor arrangement I am planning a normal mode star arrangement with the most distance sensors below 10 meters.
As for the logging, do you think a regular SD card will fail too early due to wear?
Re: Temperature monitoring project
I don't know about cutting the sleeve off the sensor, I haven't looked! It might be worth buying one to try.
As for the SD card, yes. Logging every 5 s, they seem to last between a couple of weeks and a few months. The done thing is to keep it as read-only for booting the OS, then switch everything - but everything - that involves any writes to a HDD.


The shift method removes the frist element of an array, and "shift" all other element one place up.
Try it


—
date: “2016-04-27T00:00:00Z”
external_link: "”
image:
caption: Photo by rawpixel on Unsplash
focal_point: Smart
links:
- icon: twitter
icon_pack: fab
name: Follow
url: https://twitter.com/georgecushen
slides: example
summary: An example of using the in-built project page.
tags:
- Deep Learning
title: Internal Project
url_code: "”
url_pdf: "”
url_slides: "”
url_video: "”
—
+++ title: “A” +++
Question 3.1 Plotting
The data set is divided to two sets. From the beginning until the beginning of the 2018 is considered as the Train Data set and from Jan 2018 beyond is considered the Test data-set.
#library(tidyverse)
#library(forecast)
#library(lubridate)
#library(car)
#library(scales)
#install.packages('patchwork')
#library(patchwork)
suppressMessages(library(kableExtra))
## Warning: package 'kableExtra' was built under R version 3.6.2
suppressMessages(library(knitr))
## Warning: package 'knitr' was built under R version 3.6.2
suppressMessages(library(DT))
## Warning: package 'DT' was built under R version 3.6.2
suppressMessages(library(tidyverse))
## Warning: package 'tidyverse' was built under R version 3.6.2
## Warning: package 'tidyr' was built under R version 3.6.2
## Warning: package 'purrr' was built under R version 3.6.2
suppressMessages(library(forecast))
## Warning: package 'forecast' was built under R version 3.6.2
suppressMessages(library(car))
## Warning: package 'car' was built under R version 3.6.2
suppressMessages(library(patchwork))
## Warning: package 'patchwork' was built under R version 3.6.2
suppressMessages(library(scales))
## Warning: package 'scales' was built under R version 3.6.2
suppressMessages(library(lubridate))
Data Import:
The data will of mounthly Co2 Concentration in (Part Per Million) over the period of the “1960/3-2019/12” measured at Mauna Loa Observatory, Hawaii. The link for the this data available at:
[ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt]
Now, to read this data in the R, couples of points must be considered:
- The data has the comments, which is not our interest of analysis. Therefore, in import code we show that with *comment.char = ‘#’ *,
- The data has 7 columns yet some of them like the Year and Month label have not been written in the data, so while importing we assing the folloowing column names:
- The columns wwere seperate using the white space, therfore the sep = ’’ will be added to the code.
- Year,Month,Time,Co2_con,Interpolated,Trend,Days
- Since we are including the column names, the header = F could be included.
data <- read.delim('ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt', comment.char = '#', header = F, sep = '', col.names = c('Year','Month','Time','Co2_Concentration','Interpolated','Trend','Days'))
Make Data Tidy:
Look on any NA values:
which(is.na(data))
## integer(0)
Good, we have the complete measured data! However when read the data we see some -99.99 values! - be careful, as was mentioned in the comments these values are when the measurement were not avilable - so for these points (which are 7 out of 741 measueremnets), we use the Interpolated colmns:
data_cc <- data %>%
mutate(
Co2_Con = case_when(
Co2_Concentration == -99.99 ~ Interpolated,
TRUE ~ Co2_Concentration
)
)
Let’s jhave look on column types:
sapply(data_cc, class)
## Year Month Time Co2_Concentration
## "integer" "integer" "numeric" "numeric"
## Interpolated Trend Days Co2_Con
## "numeric" "numeric" "integer" "numeric"
We can see that column types are in approriate format, yet we can add the new column named Date which give the date of measurement in the standard time sery format:
Data Transform
Here Lubridate package provides a easy method to convert our Year and Month column to date:
data_cc$Date <- ymd(paste0(data$Year, " ", data$Month, " ", "15"))
Also, we can see in the analysis we want to do, we do not the following columsn, so we could select the requered column needed our analys:
data_cc_sel <- data_cc %>%
select(Year, Month, Date, Co2_Con )
Also, we need to have portion of our data, to test the model we develop based on the training data- So, Here, we consider the data for 2017, 2018 and 2019 as the test data, the rest are the training data.
data_cc_sel_test <- data_cc_sel %>%
filter(Year > 2016)
data_cc_sel_train <- data_cc_sel %>%
filter(Year <= 2016)
Data Visulization
Now, let’s visulize the data first,
ggplot(data_cc_sel,aes(Date, Co2_Con)) +
geom_line(color='blue') +
xlab("Year, Month") +
scale_x_date(date_labels = "%Y-%m", date_breaks = "5 year") +
theme(axis.text.x = element_text(face = "bold", color = "#993333",
size = 12, angle = 45, hjust = 1)) +
ylab("CO2 Concentration (ppm)") +
#scale_x_continuous(breaks = trans_breaks(identity, identity, n = 10))
scale_y_continuous() +
theme(axis.text.y = element_text(face = "bold", color = "#993333",
size = 10, hjust = 1),axis.title.y = element_text(size = 10))
p1 <- ggplot(data_cc_sel,aes(Date, Co2_Con)) +
geom_line(color='blue') +
xlab("Year, Month") +
scale_x_date(date_labels = "%Y-%m", date_breaks = "5 year") +
theme(axis.text.x = element_text(face = "bold", color = "#993333",
size = 12, angle = 45, hjust = 1)) +
ylab("CO2 Concentration (ppm)") +
#scale_x_continuous(breaks = trans_breaks(identity, identity, n = 10))
scale_y_continuous() +
theme(axis.text.y = element_text(face = "bold", color = "#993333",
size = 10, hjust = 1),axis.title.y = element_text(size = 8))
p2 <- ggplot(data_cc_sel_train,aes(Date, Co2_Con)) +
geom_line(color='blue') +
xlab("Year, Month") +
scale_x_date(date_labels = "%Y-%m", date_breaks = "5 year") +
theme(axis.text.x = element_text(face = "bold", color = "#993333",
size = 12, angle = 45, hjust = 1)) +
ylab("CO2 Concentration (ppm)") +
#scale_x_continuous(breaks = trans_breaks(identity, identity, n = 10))
scale_y_continuous() +
theme(axis.text.y = element_text(face = "bold", color = "#993333",
size = 10, hjust = 1), axis.title.y = element_text(size = 8))
p3 <- ggplot(data_cc_sel_test,aes(Date, Co2_Con)) +
geom_line(color='blue') +
xlab("Year, Month") +
scale_x_date(date_labels = "%Y-%m", date_breaks = "1 year") +
theme(axis.text.x = element_text(face = "bold", color = "#993333",
size = 12, angle = 45, hjust = 1)) +
ylab("CO2 Concentration (ppm)") +
#scale_x_continuous(breaks = trans_breaks(identity, identity, n = 10))
scale_y_continuous() +
theme(axis.text.y = element_text(face = "bold", color = "#993333",
size = 10, hjust = 1), axis.title.y = element_text(size = 8))
(p2 | p3 ) /
p1
Modeling:
In time series analysis, first things we need to know about the trends are:
- Is the data staionary?
- Answer: Not, we see the clear trend in in the plot, so the co2 concentration depends on time. (sig of non -stationary)
- Is there any seasonality in data?
- Answer: Yes, we can difintely see the seasonality in the data. Now, knowing the the non-statinary and seasonality of the data, it suggest to use theseasons differencing to model the data. To answer,
- How is Autocorelation function and Partial Auto corellation?
Here is the plot of ACF and PACF from the forecast package:
Co2_train <- ts(data_cc_sel_train$Co2_Con, start = c(1958,3), frequency = 12)
Co2_train %>% ggtsdisplay()
Clearly the the data shows the differencing, now we make the ordinary diffrencing of the with the lag of 12:
#Co2_train %>% diff(1,lag=12) %>% ggtsdisplay()
Co2_train %>% diff(lag=12) %>% diff() %>% ggtsdisplay()
Now, it is better, we subtanitally removed the trend and as well the ACF is declining until the lag =12. At this stage, we can go for another diffrencing , but it is choice and there is no clear distinction. We start the model with the d = D = 1 in the $ARIMA(p,d,q)(P,D,Q)[12] using the forecast package.
Now, we must have some starting parameters for p,q,D,Q . So, let’s look on the above ACF and PACF: * In the seasonal lags, there is one significant spike in the ACF, suggesting a possible MA(1) term. so, the starting point is Q = 1
- In the plots of the non-seasonal differenced data, there are three spikes at ACF plot, this may be suggestive of a seasonal MA(3) term, q = 3
Cosequently, we start withe the ARIMA(0, 1, 3)(3, 1, 1)[12] and make variations in the AR and MA terms. Here, while keeping the order constant (d,D), we use the AICs values to judge the quelity of models. (minimize the AICs)
(fitnew_1 <- Arima(Co2_train, order=c(0,1,3),seasonal=list(order=c(3,1,1),period=12),
include.drift = T, lambda = "auto"
))
## Warning in Arima(Co2_train, order = c(0, 1, 3), seasonal = list(order = c(3, :
## No drift term fitted as the order of difference is 2 or more.
## Series: Co2_train
## ARIMA(0,1,3)(3,1,1)[12]
## Box Cox transformation: lambda= 0.7524011
##
## Coefficients:
## ma1 ma2 ma3 sar1 sar2 sar3 sma1
## -0.3652 -0.0294 -0.0747 0.0075 -0.0237 0.0042 -0.8748
## s.e. 0.0389 0.0415 0.0394 0.0460 0.0443 0.0442 0.0253
##
## sigma^2 estimated as 0.005415: log likelihood=831.66
## AIC=-1647.33 AICc=-1647.12 BIC=-1611
checkresiduals(fitnew_1, lag=36)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(0,1,3)(3,1,1)[12]
## Q* = 29.825, df = 29, p-value = 0.4228
##
## Model df: 7. Total lags used: 36
aicsvalue <- function(p,q,P,Q) {
fit <- Arima(Co2_train, order=c(p,1,q),seasonal=list(order=c(P,1,Q),period=12),
lambda = "auto"
)
return(fit$aicc)
}
model_eva <- data.frame(Model_name=c("ARIMA(0,1,3)(3,1,1)[12]","ARIMA(0,1,1)(3,1,1)[12]","ARIMA(1,1,0)(1,1,0)[12]",
"ARIMA(1,1,2)(1,1,0)[12]","ARIMA(1,1,3)(0,1,1)[12]","ARIMA(1,1,1)(1,1,0)[12]",
"ARIMA(1,1,1)(1,1,0)[12]","ARIMA(1,1,0)(1,1,1)[12]","ARIMA(1,1,1)(0,1,1)[12]" ), AICc=c(aicsvalue(0,3,3,1),aicsvalue(0,1,3,1),aicsvalue(1,0,1,0), aicsvalue(1,2,1,0),aicsvalue(1,3,0,1),aicsvalue(1,1,1,0), aicsvalue(1,1,1,0),aicsvalue(1,0,1,1), aicsvalue(1,1,0,1)))
Based on the above abalysis, the ARIMA(1, 1, 1)(0, 1, 1)[12] will be selected, but we need to check the residual to avoid any over and under fitting as well, to see the Ljung-Box test whether the the residuals resembles white noise or not.
(fit_minaicc <- Arima(Co2_train, order=c(1,1,1),seasonal=list(order=c(0,1,1),period=12),
lambda = "auto"
))
## Series: Co2_train
## ARIMA(1,1,1)(0,1,1)[12]
## Box Cox transformation: lambda= 0.7524011
##
## Coefficients:
## ar1 ma1 sma1
## 0.2022 -0.5638 -0.8755
## s.e. 0.0937 0.0792 0.0196
##
## sigma^2 estimated as 0.005407: log likelihood=830.21
## AIC=-1652.41 AICc=-1652.36 BIC=-1634.25
checkresiduals(fit_minaicc, lag=36)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(1,1,1)(0,1,1)[12]
## Q* = 34.18, df = 33, p-value = 0.4108
##
## Model df: 3. Total lags used: 36
fit_minaicc$aicc
## [1] -1652.355
Now, we can see the residualy sufficiently resembles the white noise also the p value high and the model pass the test for Ljong-Box test. (However it must be mentioned the one of the ACF are just reach the boundary in blue line, yet, I do not think it will affect the prediction subtantially - sometime it is difffuclt to have model pass al test.)
However, still this is not the end of model selection. Here, we check the perfomance of the model on the Test data. We ssek to find the model which minime the RMSE.
Co2_test <- ts(data_cc_sel_test$Co2_Con, start = c(2017,1), frequency = 12)
mm <- accuracy(forecast(fit_minaicc,h=35)$mean, Co2_test )
This section compares the RMSE values for the 9 models provided in the previous section.
rmse_eva <- function(p,d,q,P,D,Q) {
fit <- Arima(Co2_train, order=c(p,d,q),seasonal=list(order=c(P,D,Q),period=12),
lambda = "auto"
)
mm <- accuracy(forecast(fit,h=35)$mean, Co2_test)
return(mm[2])
}
rmse_eva <- data.frame(Model_name=c(
"ARIMA(0,1,3)(3,1,1)[12]","ARIMA(0,1,1)(3,1,1)[12]","ARIMA(1,1,0)(1,1,0)[12]",
"ARIMA(1,1,2)(1,1,0)[12]","ARIMA(1,1,3)(0,1,1)[12]","ARIMA(1,1,1)(1,1,0)[12]",
"ARIMA(1,1,1)(1,1,0)[12]","ARIMA(1,1,0)(1,1,1)[12]","ARIMA(1,1,1)(0,1,1)[12]"
), RMSE=c(
rmse_eva(0,1,3,3,1,1),rmse_eva(0,1,1,3,1,1),rmse_eva(1,1,0,1,1,0), rmse_eva(1,1,2,1,1,0),rmse_eva(1,1,3,0,1,1),rmse_eva(1,1,1,1,1,0), rmse_eva(1,1,1,1,1,0),rmse_eva(1,1,0,1,1,1),rmse_eva(1,1,1,0,1,1)))
The results show that the the model ARIMA(1, 1, 1)(0, 1, 1)[12] has not the minimum RMS**E values, yet it was very close to the minimum, however it was minimum in the AIC**c values. Atbthe end, knowing that the model residuals foloow the white noise, the RMS**E is the final criteria to the selection since it performs on the data the model has not seen in the training process. Using the forecast package, the figures shows the model prediction until the 2050, given the confidence intervals.
Co2_train %>%
Arima(order=c(1,1,1),seasonal=list(order=c(0,1,1),period=12),
lambda = "auto"
) %>%
forecast(h=400) %>%
autoplot() +
ylab("H02 sales (million scripts)") + xlab("Year") +
autolayer(Co2_test)
Let’s zoom in the model prediction and the test data to see the model perfomance visually:
prediction <- forecast(fit_minaicc,h=37)
data_cc_sel_test$prediction <- prediction$mean
data_test_pre_tidy <- gather(data_cc_sel_test, "type", "Co2", -Year,-Month,-Date)
## Warning: attributes are not identical across measure variables;
## they will be dropped
ggplot(data_test_pre_tidy,aes(Date, Co2,color=type)) +
geom_line() +
xlab("Year, Month") +
scale_x_date(date_labels = "%Y-%m", date_breaks = "1 year") +
theme(axis.text.x = element_text(face = "bold", color = "#993333",
size = 12, angle = 45, hjust = 1)) +
ylab("CO2 Concentration (ppm)") +
#scale_x_continuous(breaks = trans_breaks(identity, identity, n = 10))
scale_y_continuous() +
theme(axis.text.y = element_text(face = "bold", color = "#993333",
size = 10, hjust = 1), axis.title.y = element_text(size = 8))
Now, given the developed model the question we want to answer is:
Given the developed model, what is the chance reaching 460 ppm at 2050? To answer this question , we first need build the cumulative distribution of the Co2 Concentration at the 2050:
prediction1 <- forecast(fit_minaicc,h=396, level = c(80,90))
p10 <- prediction1$upper[396,2]
p50 <- prediction1$mean[396]
sd_calc <- (p10-p50)/1.28
Co2_con_2050 <- rnorm(10^6,p50,sd_calc)
cdf_co2_con_2050 <- ecdf(Co2_con_2050)
cdf_co2_con_2050_data <- data.frame(Co2_con_2050)
ggplot(cdf_co2_con_2050_data, aes(Co2_con_2050)) + stat_ecdf(geom = "step", color='blue') +
geom_vline(xintercept = 460, color='red') +
geom_hline(yintercept = cdf_co2_con_2050(460), color='red') +
theme(axis.text.x = element_text(face = "bold", color = "#993333",
size = 12, angle = 0, hjust = 1)) +
scale_x_continuous(breaks=c(400,425,450, 460,475,500,525, 550), limits = c(425,525)) +
scale_y_continuous(breaks=c(seq(0,1,0.1)), limits = c(0,1)) +
ylab('Cumulative Distribution') +
xlab("Co2 Concentraion(ppm) at 2050")
## Warning: Removed 2146 rows containing non-finite values (stat_ecdf).
Now, having the cumulative distribution, we could ask this question:
- What is the probability the co2 concentraion (ppm) will stay below 460 level by 2050?
cdf_co2_con_2050(460)
## [1] 0.088727
As you can see, the answer is around 9%.


About Me
Hi I am Senthilkumar M, I'm a Research Engineer. I live in Bangalore,India. I have been adding 1's and 0 ‘s in semiconductor industry. I am very much interested in anything that drifts electrons.I have done intensive programming on several languages and C is my all time favourite
I have done lot of work on Wireless and Network protocol development and middle-ware development. I don't want to commit myself as a system developer so my free time I am doing lots of cool website designing, developing mobile application, contributing to the open source. I believe the women have capability to work on advanced research field. So I am mentoring more than 200 girls from my native place for Technovation Challenge 2014. I have completed my bachelors degree in Electronics and communication Engineering from Anna University. Madurai is my home town which is situated in south part of Tamil nadu in India
Acadamics
Hi I am Senthilkumar M, I'm a Research Engineer. I live in Bangalore,India. I have been adding 1's and 0 ‘s in semiconductor industry. I am very much interested in anything that drifts electrons.I have done intensive programming on several languages and C is my all time favourite
I have done lot of work on Wireless and Network protocol development and middle-ware development. I don't want to commit myself as a system developer so my free time I am doing lots of cool website designing, developing mobile application, contributing to the open source. I believe the women have capability to work on advanced research field. So I am mentoring more than 200 girls from my native place for Technovation Challenge 2014. I have completed my bachelors degree in Electronics and communication Engineering from Anna University. Madurai is my home town which is situated in south part of Tamil nadu in India
Achievements
Hi I am Senthilkumar M, I'm a Research Engineer. I live in Bangalore,India. I have been adding 1's and 0 ‘s in semiconductor industry. I am very much interested in anything that drifts electrons.I have done intensive programming on several languages and C is my all time favourite
I have done lot of work on Wireless and Network protocol development and middle-ware development. I don't want to commit myself as a system developer so my free time I am doing lots of cool website designing, developing mobile application, contributing to the open source. I believe the women have capability to work on advanced research field. So I am mentoring more than 200 girls from my native place for Technovation Challenge 2014. I have completed my bachelors degree in Electronics and communication Engineering from Anna University. Madurai is my home town which is situated in south part of Tamil nadu in India
Projects
Hi I am Senthilkumar M, I'm a Research Engineer. I live in Bangalore,India. I have been adding 1's and 0 ‘s in semiconductor industry. I am very much interested in anything that drifts electrons.I have done intensive programming on several languages and C is my all time favourite
I have done lot of work on Wireless and Network protocol development and middle-ware development. I don't want to commit myself as a system developer so my free time I am doing lots of cool website designing, developing mobile application, contributing to the open source. I believe the women have capability to work on advanced research field. So I am mentoring more than 200 girls from my native place for Technovation Challenge 2014. I have completed my bachelors degree in Electronics and communication Engineering from Anna University. Madurai is my home town which is situated in south part of Tamil nadu in India
Contact
Hi I am Senthilkumar M, I'm a Research Engineer. I live in Bangalore,India. I have been adding 1's and 0 ‘s in semiconductor industry. I am very much interested in anything that drifts electrons.I have done intensive programming on several languages and C is my all time favourite
I have done lot of work on Wireless and Network protocol development and middle-ware development. I don't want to commit myself as a system developer so my free time I am doing lots of cool website designing, developing mobile application, contributing to the open source. I believe the women have capability to work on advanced research field. So I am mentoring more than 200 girls from my native place for Technovation Challenge 2014. I have completed my bachelors degree in Electronics and communication Engineering from Anna University. Madurai is my home town which is situated in south part of Tamil nadu in India


Hi! I'm Xavier Cho, I love to overlap security and software. My skills range from security operations to application security. But I do find joy in every aspect of the security landscape. I'd love to get coffee / food sometime,
so don’t hesitate to get in touch!
( ´ ▽ ` )ﾉ
Interests: Puns, Gaming, Photography, Food, Fitness & Code.
View Resume
| Languages | Infosec Toolkit | Frameworks |
|---|---|---|
| Python | HackerOne | MITRE ATT&CK |
| C++ | Mimecast / Area1 | MITRE D3FEND |
| Javascript | Crowdstrike / Carbon Black | OWASP |
| BurpSuite | SSDLC | |
| Thinkst Canary | ||
| Tines / Workato / SOAR | ||
| LogRhythm / SIEM | ||
| Detectify | ||
| JFrog / SCA | ||
| OpsLevel | ||
| MEND Renovate | ||
| TheHive Project | ||
| Darktrace | ||
| Tenable | ||
| KnowBe4 | ||
| Secure Code Warrior | ||
| Git & Github |
Here is a list to some of my related projects
down below on this site.
Want to see more?
Here is my Github
Technologies: C++, OpenGL
3D version of the Brownian motion problem, where a single particle that starts at a random position inside a volume bounded by an interactive polyhedron. The particle continues to move until it exits the volume. When the particle exits polygon through which it exits changes color to solid color and becomes opaque.
View Source Code
Technologies: C++, OpenGL
Implemented using shaders using three lighting scenarios in a 3D scene, The goal in this is to build a small scene to exercise and test the understanding of lighting and shading; The player/user is identified with the flying camera.
View Source Code
Technologies: C++, OpenGL
Shows the fundamentals of computer graphics, shaders and graphics pipeline within the scene hierarchy. Designed with texture mapping and instancing to create the terrain, forest and village. The user travels to a village in a car, during the trip the user is allowed to toggle viewpoints using the keyboard command "V".
View Source Code
Technologies: C#, Unity, ARCore
Implemented using shaders and lighting scenarios in a 3D scene, The goal with this is to build Sponza to test lighting, shading and collision detection with ARCore.
View Source Code
Technologies: C#, Unity
Implemented particle effects, collision detection, scoring system and UI. The goal with this is to build something more robust and shippable as a game demo with Unity.


Works by: Edit menu, right-click
Hot keys: None
Source requirements: One or more source relationships required
Target requirement: No target required
The Clone operation creates an exact duplicate of one or more terms, but assigns new id numbers to the duplicates. The duplicate terms have the same parents, children, namespace, definition, comments, synonyms, dbxrefs, and category assignments as the original term (alternate ids are not preserved). The name of the cloned term is "CLONE OF <the original term name>".
To clone one or more terms, select the terms and choose "Clone" from the right-click or edit menu.
Note that the Clone operation will give the cloned terms exactly the same relationships as the original source terms. If parent-child relationships exist between the source terms, parallel parent-child relationships between the cloned terms will not be created. For example, if you select a cluster of three terms where one of the terms is a parent of the other two:
After cloning, three cloned terms are created. Each of these has the same relationships as the original source terms. However, parallel parent-child relationships between the cloned terms are not created:


Image Interface¶
This file contains some common interfaces for image preprocess. Many users are confused about the image layout. We introduce the image layout as follows.
CHW Layout
- The abbreviations: C=channel, H=Height, W=Width
- The default layout of image opened by cv2 or PIL is HWC. PaddlePaddle only supports the CHW layout. And CHW is simply a transpose of HWC. It must transpose the input image.
Color format: RGB or BGR
OpenCV use BGR color format. PIL use RGB color format. Both formats can be used for training. Noted that, the format should be keep consistent between the training and inference peroid.
-
paddle.v2.image.
batch_images_from_tar
(data_file, dataset_name, img2label, num_per_batch=1024)¶ Read images from tar file and batch them into batch file.
Parameters: - data_file (string) – path of image tar file
- dataset_name (string) – ‘train’,’test’ or ‘valid’
- img2label (dic) – a dic with image file name as key and image’s label as value
- num_per_batch (int) – image number per batch file
Returns: path of list file containing paths of batch file
Return type: string
-
paddle.v2.image.
load_image_bytes
(bytes, is_color=True)¶ Load an color or gray image from bytes array.
Example usage:
with open('cat.jpg') as f: im = load_image_bytes(f.read())
Parameters: - bytes (str) – the input image bytes array.
- is_color (bool) – If set is_color True, it will load and return a color image. Otherwise, it will load and return a gray image.
-
paddle.v2.image.
load_image
(file, is_color=True)¶ Load an color or gray image from the file path.
Example usage:
im = load_image('cat.jpg')
Parameters: - file (string) – the input image path.
- is_color (bool) – If set is_color True, it will load and return a color image. Otherwise, it will load and return a gray image.
-
paddle.v2.image.
resize_short
(im, size)¶ Resize an image so that the length of shorter edge is size.
Example usage:
im = load_image('cat.jpg') im = resize_short(im, 256)
Parameters: - im (ndarray) – the input image with HWC layout.
- size (int) – the shorter edge size of image after resizing.
-
paddle.v2.image.
to_chw
(im, order=(2, 0, 1))¶ Transpose the input image order. The image layout is HWC format opened by cv2 or PIL. Transpose the input image to CHW layout according the order (2,0,1).
Example usage:
im = load_image('cat.jpg') im = resize_short(im, 256) im = to_chw(im)
Parameters: - im (ndarray) – the input image with HWC layout.
- order (tuple|list) – the transposed order.
-
paddle.v2.image.
center_crop
(im, size, is_color=True)¶ Crop the center of image with size.
Example usage:
im = center_crop(im, 224)
Parameters: - im (ndarray) – the input image with HWC layout.
- size (int) – the cropping size.
- is_color (bool) – whether the image is color or not.
-
paddle.v2.image.
random_crop
(im, size, is_color=True)¶ Randomly crop input image with size.
Example usage:
im = random_crop(im, 224)
Parameters: - im (ndarray) – the input image with HWC layout.
- size (int) – the cropping size.
- is_color (bool) – whether the image is color or not.
-
paddle.v2.image.
left_right_flip
(im, is_color=True)¶ Flip an image along the horizontal direction. Return the flipped image.
Example usage:
im = left_right_flip(im)
Parameters: - im (ndarray) – input image with HWC layout or HW layout for gray image
- is_color (bool) – whether input image is color or not
-
paddle.v2.image.
simple_transform
(im, resize_size, crop_size, is_train, is_color=True, mean=None)¶ Simply data argumentation for training. These operations include resizing, croping and flipping.
Example usage:
im = simple_transform(im, 256, 224, True)
Parameters: - im (ndarray) – The input image with HWC layout.
- resize_size (int) – The shorter edge length of the resized image.
- crop_size (int) – The cropping size.
- is_train (bool) – Whether it is training or not.
- is_color (bool) – whether the image is color or not.
- mean (numpy array | list) – the mean values, which can be element-wise mean values or mean values per channel.
-
paddle.v2.image.
load_and_transform
(filename, resize_size, crop_size, is_train, is_color=True, mean=None)¶ Load image from the input file filename and transform image for data argumentation. Please refer to the simple_transform interface for the transform operations.
Example usage:
im = load_and_transform('cat.jpg', 256, 224, True)
Parameters: - filename (string) – The file name of input image.
- resize_size (int) – The shorter edge length of the resized image.
- crop_size (int) – The cropping size.
- is_train (bool) – Whether it is training or not.
- is_color (bool) – whether the image is color or not.
- mean (numpy array | list) – the mean values, which can be element-wise mean values or mean values per channel.


External and Internal Brains
The External and Internal types of Brains work in different phases of training. When training your agents, set their brain types to External; when using the trained models, set their brain types to Internal.
External Brain
When running an ML-Agents training algorithm, at least one Brain object in a scene must be set to External. This allows the training process to collect the observations of agents using that brain and give the agents their actions.
In addition to using an External brain for training using the ML-Agents learning algorithms, you can use an External brain to control agents in a Unity environment using an external Python program. See Python API for more information.
Unlike the other types, the External Brain has no properties to set in the Unity Inspector window.
Internal Brain
The Internal Brain type uses a TensorFlow model to make decisions. The Proximal Policy Optimization (PPO) and Behavioral Cloning algorithms included with the ML-Agents SDK produce trained TensorFlow models that you can use with the Internal Brain type.
A model is a mathematical relationship mapping an agent's observations to its actions. TensorFlow is a software library for performing numerical computation through data flow graphs. A TensorFlow model, then, defines the mathematical relationship between your agent's observations and its actions using a TensorFlow data flow graph.
Creating a graph model
The training algorithms included in the ML-Agents SDK produce TensorFlow graph models as the end result of the training process. See Training ML-Agents for instructions on how to train a model.
Using a graph model
To use a graph model:
- Select the Brain GameObject in the Hierarchy window of the Unity Editor. (The Brain GameObject must be a child of the Academy GameObject and must have a Brain component.)
Set the Brain Type to Internal.
Note: In order to see the Internal Brain Type option, you must enable TensorFlowSharp.
Import the
environment_run-id.bytes
file produced by the PPO training program. (Whereenvironment_run-id
is the name of the model file, which is constructed from the name of your Unity environment executable and the run-id value you assigned when running the training process.)You can import assets into Unity in various ways. The easiest way is to simply drag the file into the Project window and drop it into an appropriate folder.
Once the
environment.bytes
file is imported, drag it from the Project window to the Graph Model field of the Brain component.
If you are using a model produced by the ML-Agents learn.py
program, use the default values for the other Internal Brain parameters.
Internal Brain properties
The default values of the TensorFlow graph parameters work with the model produced by the PPO and BC training code in the ML-Agents SDK. To use a default ML-Agents model, the only parameter that you need to set is the Graph Model
, which must be set to the .bytes file containing the trained model itself.
Graph Model
: This must be thebytes
file corresponding to the pre-trained TensorFlow graph. (You must first drag this file into your Resources folder and then from the Resources folder into the inspector)
Only change the following Internal Brain properties if you have created your own TensorFlow model and are not using an ML-Agents model:
Graph Scope
: If you set a scope while training your TensorFlow model, all your placeholder name will have a prefix. You must specify that prefix here. Note that if more than one Brain were set to external during training, you must give aGraph Scope
to the internal Brain corresponding to the name of the Brain GameObject.Batch Size Node Name
: If the batch size is one of the inputs of your graph, you must specify the name if the placeholder here. The brain will make the batch size equal to the number of agents connected to the brain automatically.State Node Name
: If your graph uses the state as an input, you must specify the name of the placeholder here.Recurrent Input Node Name
: If your graph uses a recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if the input placeholder here.Recurrent Output Node Name
: If your graph uses a recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if the output placeholder here.Observation Placeholder Name
: If your graph uses observations as input, you must specify it here. Note that the number of observations is equal to the length ofCamera Resolutions
in the brain parameters.Action Node Name
: Specify the name of the placeholder corresponding to the actions of the brain in your graph. If the action space type is continuous, the output must be a one dimensional tensor of float of lengthAction Space Size
, if the action space type is discrete, the output must be a one dimensional tensor of int of length 1.Graph Placeholder
: If your graph takes additional inputs that are fixed (example: noise level) you can specify them here. Note that in your graph, these must correspond to one dimensional tensors of int or float of size 1.Name
: Corresponds to the name of the placeholder.Value Type
: Either Integer or Floating Point.Min Value
andMax Value
: Specify the range of the value here. The value will be sampled from the uniform distribution ranging fromMin Value
toMax Value
inclusive.


REGISTER
Price
Since we’re student-run, we understand what it’s like to have a student’s budget. This is why we keep registration at a low price of $30: the best deal you’ll get for any design conference.
And hey! If you take part in the early-bird registration, the cost is only $25.
What Should I Bring?
Design portfolio
We offer an annual Portfolio Review, hosted by our speakers and other local design professionals. Now’s your chance to get your work reviewed by contemporary designers before you begin the job search.
Business Cards
This isn’t all lectures: Thought at Work offers lots of opportunities to hang out and chill with professionals, as well as students from other design colleges. Feel free to bring anything you’d want to give or show to other designers.
Yourself!
Come ready for 3 full days of education, creativity, and fun.
Display Your Work
When you register for Thought at Work, there will be an option for you to submit student projects. Because this event is all about bringing students together, we’ll have an electronic slideshow on display which will showcase student work from anyone attending. This will be a great chance for you to see how other schools focus their curriculums and address design.
You will be able to attach files of any work you’re proud of, and we’ll happily include it in the slideshow. We accept entire portfolios as well as single projects. Your name and school (year level, major) will be shown alongside your work. Files will be accepted in PDF, jpeg, and png format. If you have any questions, please contact us via the email address listed below.
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A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
Hollowman900014-Oct-18 12:42 AM
@Geocold51 again thanks for showing up, if you hang around you may notice we’re pretty much on the leading edge of crypto tech
You’ll always be welcome here
PTYX14-Oct-18 12:43 AM
I guess a 51% attack could be considered a hack and only ethical if the attacker has permission from the victim. that's probably why you got shut down.

Geocold5114-Oct-18 12:51 AM
There is only a single wallet
Waht did they hint me to do?
I wasn't reading chat
well
i mean ofc a keypool of 100
but
Only one with funds
Hollowman900014-Oct-18 12:52 AM
At least you caught my Pirate ($ARRR) offer
Alright14-Oct-18 12:53 AM
my point is that you need to verify that you're spending the same UTXO on both chains
Geocold5114-Oct-18 12:53 AM
Yes
I'm aware
That's why I checked transactions to the wallet
Alright14-Oct-18 12:53 AM
also at the beginning you said, something like it matters which one happens first
it doesn't
Geocold5114-Oct-18 12:53 AM
to check that there was only one address with funds
what?
No I didn't
0x0314-Oct-18 12:53 AM
you did
Hollowman900014-Oct-18 12:53 AM
@jl777c wake up
Geocold5114-Oct-18 12:53 AM
you need to send the one on the private chain first
Alright14-Oct-18 12:53 AM
no doesn't matter
0x0314-Oct-18 12:54 AM
Alright14-Oct-18 12:54 AM
seems like it would, but it doesn't
Geocold5114-Oct-18 12:54 AM
ohhh
sure
PTYX14-Oct-18 12:54 AM
I think we were all just expecting something a little more professional.
Geocold5114-Oct-18 12:54 AM
why?

I care if it works
0x0314-Oct-18 12:56 AM
mate
Geocold5114-Oct-18 12:56 AM
I'm using the coin for an hour or so
Alright14-Oct-18 12:56 AM
chill man :/
0x0314-Oct-18 12:56 AM
3 hours into your so called 'attack' and all you managed was to get a NH-order up against BTCP
Geocold5114-Oct-18 12:56 AM
Nah
Alright14-Oct-18 12:56 AM
not trying to troll you or anything
0x0314-Oct-18 12:56 AM
i am
hard af
Geocold5114-Oct-18 12:56 AM
I had an offline chain with a conflicitng transactio


Leveraging the underlying open source technology InSpec, enterprises needed a way to manage large compliance reports (datasets) and communicate control implementation and stature across their organization.
hypothesis and solutionDevelop a 508 compliant application that allows enterprises to continously monitor their compliance stature, take action and implement controls across their entire hybrid infrastructure.
key responsibilitiesUser research, usability testing in EA and Beta, feature definition, big data visualization, visual design, web component development and UX.
The process of developing the end-to-end UX for the Chef Compliance app required me to learn about the day to day tasks of our users. Our users consisted of system administrators and security engineers who spent a significant time on the commandline. It was important to understand the deficiancies of using the terminal in their workflow but also offer feature parody between the terminal and the web UI. I spent countless hours talking with our enterprise users in order to understand their pain points, business needs from managers and requirements from an infrastructure maturity perspective - which were then storymapped into an end-to-end story including installation, RBAC, user management, collaboration and asset management, and compliance failure correlation.


This is a list of ideas posted by other awesome people like you. Comment and upvote
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--- layout: post title: Yudansha gradings date: 2006-10-22 00:00:00 category: aikido ---
Jon has posted some video clips of the yudansha gradings at last years Aikido Summer School.


Pizza Block
Large-pizza
ksh.2000/=
ADD TO CART
Peproni
Mushrooms
Onions
Bacon
Extra Cheese
Sausage
Green Pepper
Medium-pizza
ksh.1800.00/=
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Peproni
Mushrooms
Onions
Bacon
Extra Cheese
Sausage
Green Pepper
Small-pizza
ksh.900.00=
ADD TO CART
Peproni
Mushrooms
Onions
Bacon
Extra Cheese
Sausage
Green Pepper
CART
ITEM
PRICE
QUANTITY
Small-pizza
ksh 900.00/=
REMOVE
Medium-pizza
ksh1200.00/=
REMOVE
Total
ksh 2100.00/=
PURCHASE
For Delivery
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Saika
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Online Book Store Project” is a simple online Book store app using React Js, I had not used redux in it (still have to learn it) is only React js, the backend server is hosted using Node js, it took me almost one month to finish this project, initially I planned to add more functionality to it but the amount of code (long list of state & props) was just increasing and I was overwhelmed with it, I will include this project in my portfolio, & it may not look nice in mobile devices as spent less time to make it responsive. The admin can only delete and edit the books that has been added by the admin. Publisher category contain all the publishers lists. The two main things are not fully implemented is contact and process purchase. Due to having to work with some security and online payment, the process site is just a place holder. Contacts category content the name email and the text area which helps admin to get feedback from the users.


Star Wars Jedi Fallen Order terrarium guide: Find all seeds
by Steve WrightFancy a bit of gardening?
Star Wars Jedi Fallen Order is out now, and one of its Achievments or Trophies tasks young Jedi Knights with finding terrarium seeds in order to help out pilot Greez. This guide will show you how to do just that!
All up, there are 10 seeds that not only have to be planted in the terrarium on the Mantis, but given enough time to grow. Doing all that will net you the Green Thumb Achievement or Trophy.
Seed locations are below, listed in the order they appear in the terrarium.
Fetherfern seed location
This is on Bogano in the Fractured Plain. Look for a plant with purple flowers.
Kalpi seed location
This is also on Bogano in the Subterranean Refuge.
Dreamwort seed location
Dreamwort is found on Zeffo in the Crash Site area. This is on the large area in the middle, on the coast just off a wrecked ship. Watch out for the baddies before you try to collect this plant with yellow flowers.
Royal Fuzz seed location
This is on Zeffo in the Weathered Monument area. This is near a chest that you’ll need BD-1 to slice. Look for a purple flowered plant.
Gillypod seed location
This is at Zeffo in the Windswept Ruins. Look for a red flowering plant at the top of a raised stone area.
Milk Grass seed location
This is on Kashyyyk in the Gloomroot Hollow area. Look for a giant plant with thick, green stalks.
Mushbloom seed location
This is on Kashyyyk in the Origin Tree area. From a mediation point, climb up a nearby ledge and look to yellow, bulbous plants at the base of one of those green plants you can use as a trampoline.
Bonshyyyr seed location
This is on Kashyyyk in the Imperial Refinery. You’ll find this near a mediation spot where Stormtroopers are fighting Giant Spiders. This is a small green plant nestled against some dead ferns.
Bleeding Gut seed location
The remaining two plants are found on Dathomir. The first is in the Swamp of Sacrifice. Head towards the bottom of the big central hub area and follow a red vine to a giant red flower.
Mushling seed location
The final plant is also on Dathomir in the Swamp of Sacrifice. This one’s in the same relative area; look to the wall near a cliff that’s populated with Nightsister pods.
Work better off a video? Easy done — an excellent one from PS4 Trophies is below. We’ve also used some images from the video above after we discovered some of our capture went haywire.
Star Wars Jedi Fallen Order is out now on Windows PC, Xbox One and PS4.


 Why you’ll never succeed at music videos. 20 amazing country song ringtone pictures. What experts are saying about popular songs. The 18 worst dance playlists in history. How country song ringtones make you a better lover. 12 myths uncovered about piano stores. The 19 best country billboard twitter feeds to follow.
14 bs facts about billboard alternatives everyone thinks are true. How to be unpopular in the hot song world. What everyone is saying about concert tickets. The unconventional guide to concert tickets. 15 podcasts about country billboards. Why rock fame will make you question everything. 16 things about hot songs your kids don’t want you to know. Will billboard alternatives ever rule the world? 8 insane (but true) things about hot songs. How hollywood got country music festivals all wrong.
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Hi, my name is Tianpeng, I'm currently studying my master degree in mathematics at University of Technology, Sydney. I have particular interests in computing & engineering stuff and love doing intriguing project. Recently, I got caught into the beauty of AI technologies like: Machine Learning, Deep Learning and Reinforcement Learning etc, and its real-life applications, i.e. Self-Driving Car. I wish to make heaps of interesting AI (not very AGI) projects and applications in the future. I hope you have a good time browsing here and feel free to chat me at any time.
This project involves the design and implementation of an Online Movies Database (OMDB). OMDB maintains information about movies being screened in cinemas near users. The database supports a website that allows the users to query this information and search for movies and cinema sessions. Each movie is screened in one or more cinemas. The event listings show cinema sessions for each movie for one week (i.e. movies screened at a given cinema for seven days, starting today).
Aerospace Department of USYD has started to develop Mircro-UAV to serve multi-purposes. One of the research interests is to assist the functionality of the Planetary Rover developed by the ACFR. Hence, in this project, an open-platform micro-UAV/quadcopter called Crazyflie, developed in Sweden, will be studied. In addition, Crazyflie will also be used to test out the feasibility of automatically tracking flight with motion camera.
This project is to design a Autonomous Delivery Vehicle as a group of 7 to fulfill the Flying Donkey Challenge. The Flying Donkey Challenge is a design competition for an unmanned aerial vehicle with the purpose of cargo delivery around Mount Kenya. The proposed ADV features design characteristics which are suited for the target mission. Initial performance constraints were found to be the take-off power requirement and this was addressed with employing twin engines with high power to weight ratios. Space requirements due to the large cargo size was addressed by employing high mounted wings which also played a role in minimising FOD from the cleared dirt runways.
Intro
Intro
Haha
Yes,haha


Pacing index embed code generator
select course
select term
link to published Google Slides
instance
link
embed


Classes in this namespace enable you to interact with the Windows Azure environment from code running within your role.


#import "../layout/defaultLayout.ftl.html" as layout><EMAIL_ADDRESS>"Home page">
and welcome to PIVOT.Bots - an app for automatic chatbot-based assessment.
Our bots learn from domain-specific text you provide by extracting keywords and questions.
In the dialogue later, they evaluate users based on measuring associated sentiments in the answers given.


A simple calculator for a simple (wo)man.
I present to you the...
SimplCalc 5000
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AC
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This is a project from my journey through the wonderful
FreeCodeCamp
bootcamp.


Sprites
loading two images as one and then changing the position like for a hover or focus.
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Selectors
Pseudo Classes: :hover / :focus / :active / :visited :first-child / :last-child / :only-child :nth-child() / :nth-of-type()
Pseudo Elements: :before / :after :first-line / :first-letter
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li:nth-child(an+b){} a = the intervals b = the position (zero if it isn’t set)
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Adjacent sibling selectors: : have the following syntax: E1 + E2, where E2 is the subject of the selector. The selector matches if E1 and E2 share the same parent in the document tree and E1 immediately precedes E2, ignoring non-element nodes (such as text nodes and comments).
Thus, the following rule states that when a P element immediately follows a MATH element, it should not be indented:
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Relative lengths em unit : Equal to the computed value of the ‘font-size’ property of the element on which it is used. ex unit : Equal to the font’s x-height.
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links: - CSS Tricks- Pseudo Class Selectors - Amazing stuff pseudo-elements can do


Choose a photo for your profile picture:
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Chorus is a slick web interface focused on using Kodi as a shared player.
Created by Jeremy @ github.com/jez500
Do you like Chorus? You can <%= app.ui.beerLink('buy me a beer') %> to say thanks


nips nips2003 nips2003-137 nips2003-137-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yoshua Bengio, Yves Grandvalet
Abstract: Most machine learning researchers perform quantitative experiments to estimate generalization error and compare algorithm performances. In order to draw statistically convincing conclusions, it is important to estimate the uncertainty of such estimates. This paper studies the estimation of uncertainty around the K-fold cross-validation estimator. The main theorem shows that there exists no universal unbiased estimator of the variance of K-fold cross-validation. An analysis based on the eigendecomposition of the covariance matrix of errors helps to better understand the nature of the problem and shows that naive estimators may grossly underestimate variance, as con£rmed by numerical experiments. 1
[1] Y. Bengio and Y. Grandvalet. No unbiased estimator of the variance of K-fold cross-validation. Journal of Machine Learning Research, 2003.
[2] L. Breiman. Heuristics of instability and stabilization in model selection. The Annals of Statistics, 24(6):2350–2383, 1996.
[3] L. Devroye, L. Gy¨ r£, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer, o 1996.
[4] T. G. Dietterich. Approximate statistical tests for comparing supervised classi£cation learning algorithms. Neural Computation, 10(7):1895–1924, 1999.
[5] B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap, volume 57 of Monographs on Statistics and Applied Probability. Chapman & Hall, 1993.
[6] M. Kearns and D. Ron. Algorithmic stability and sanity-check bounds for leave-one-out crossvalidation. Neural Computation, 11(6):1427–1453, 1996.
[7] R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Arti£cial Intelligence, pages 1137–1143, 1995.
[8] C. Nadeau and Y. Bengio. Inference for the generalization error. Machine Learning, 52(3):239– 281, 2003.
[9] M. Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, B, 36(1):111–147, 1974.


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| 7 |
Potential harm or risk to participants |
blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah bl
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Risk management strategy |
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Will participants be given financial or non-financial incentives? |
Yes blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah
blah blah blah blah blah blah blah blah blah blah blah blah
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Please select how you are going to recruit participants and in the space below describe your recruitment process. |
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Will participants provide consent? |
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| 12 |
Does the research use deception, concealment, incomplete disclosure, limited disclosure, an opt- out approach, or use of information, samples, health information etc., without the specified consent from those persons? |
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Is your study a clinical trial? |
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Will a placebo/non-treatment group be used? |
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Has this trial been registered? |
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Are the facilities, expertise and experience available sufficient for the trial to be conducted safely? |
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Does your Participant Information Statement make clear to the participant whether they will have continued access after the trial to treatment they have received during the trial, and on what terms? |
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| 14 |
Does your research use health information that may reveal information that may be important for the health or future health of the donor(s), their blood relatives or their community? |
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| 15 |
Does your research involve human genetics? |
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| 16 |
Does your research involve women who are pregnant and/or the human fetus? |
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| 16a |
Will steps be taken to ensure that the well-being and care of the woman who is pregnant and her fetus takes precedence over the aims of the research? |
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| 16b |
Will the information about research be separate from information about routine clinical care? |
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| 17 |
Does your research involve children and young people? |
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| 17a |
Do you have a Working With Children's (WWC) card? |
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| 18 |
Does your research involve people in dependent or unequal relationships? |
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| 19 |
Does your research involve people highly dependent on medical care who may be unable to give consent? |
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Does your research involve people with a cognitive impairment, an intellectual disability, or a mental illness? |
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Does your research involve people who may be involved in illegal activities? |
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| 22 |
Does your research involve Aboriginal and Torres Strait Islanders? |
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| 22a |
What is the estimated proportion of Aboriginal and Torres Strait Islanders in the population from which the participants will be recruited? |
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| 22b |
Will Aboriginal and Torres Islander status of participants be recorded? |
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Accessibility
The Distance Formula
The Tyrany of Text
Cultural Inequalities
Our mathematics education system is inequitable. It operates in ways that leave a significant proportion of students with negative mathematics experiences and inadequate mathematical preparation. The problem is historical and systemic, and the students most disaffected by the current system are overwhelmingly Black and Latino, Indigenous, poor, women, immigrant or first generation college students. If our mathematics community is to sustainably grow and thrive, mathematics education at all levels must be transformed.
Poverty
Our most recent podcast — “Smarter Kids at 10 Bucks a Pop” — was about a pair of economists who found a cheap and effective way to boost schoolkids’ learning in China: find the kids who need glasses and give them a pair. Voila! The economists found that between 10 and 15 percent of kids needed glasses; of those who needed them, only two percent had them.
Don't forget to support the supporters
Some math professors only write by hand. Capture their knowledge with new technology, and then transfer the now digitalized content to an accessible form for the student.
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Equation Editor and MathType Tips to Make Your Life Easier 9.pdf)
We will not use the "new" Equation Editor in Word 2007 or Office 201. The "old" one is still available in all Office 2007/2010 applications, and this is the one we'll be using.
As accessed 14 October 2019
Resources
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WAI-ARIA Practices WD math example
a pen by Peter Krautzberger | as accessed October 1, 2019