a)
Package wrapping technique can be used to compute convex hull of a set Q given by Jarvis march.
The running time of the algorithm runs in O(nh), where h is the number of vertices of CH (Q).
The total running time for K convex layer and the ith layer contains li points is, since,
b)
Linear time reduction for sorting problem:
• The convex layers can be computed in linear time for give numbers in sorted order.
• Consider convex layer A with 3 points into set Q for each A[k]: (0, 0), (k, 0), and (0, A[k]).
• Let Q be with n convex layers and Qi represents a triangle with vertices (0, 0), (n+i-1, 0), and (0, B[i]).
• Therefore, given convex layer can be converted into its corresponding sorted value.
• Since, the lower bound is known to find convex layers; it could be easy to determine convex layers in O(n log n) time, which could be sorted in O(n log n) time.
Hence, a convex layer takes time.
MAXIMAL LAYER
In order to define the maximal layer, consider a set Q which has n points in the given plane. It is required to demonstrate the dominance level between any two points, that is a point is said to be dominant over the point if the condition and is fulfilled.
A point in the given set Q is said to be maximal or maximum if there does not exist any point which dominates over that point. A set can have a number of maximal points which can be grouped together to form a maximal layer which is illustrated as follows-the first maximal layer is denoted as which have the set of maximal points.
For the value of, the ith maximal layer can be defined as which is set of the maximal points which are defined as:
a. Given that the number of the non-empty layers present in set Q is K, is supposed to be the y- coordinates of the left-most point in for. It is also assumed that no two points in the Q have the same x-or y- coordinate.
Here, it is required to show that
During the extraction of maxima layer when the layers are restored after the last iterations during the maxima layer algorithm, the y-layer order is need to be restored.
In the process, a problem with the concern of memory size is there for each layer and due to this reason it is not possible to sort the point easily.
As the maxima layer for an n-element point for the two dimensions are to be computed and have the running time of in such a way that for each layers the points are sorted in the decreasing order of their y-coordinates.
Hence the orders of the y-coordinates are as follows:
b. Given that j is the minimum index provided that the and, in the case it is supposed that and for Q’ the condition will hold which is as,
? if the equality holds then the maximal layer of Q’ will have the condition that, a set can have a number of maximal points which can be grouped together to form a maximal layer which is illustrated as follows,
The first maximal layer is denoted as which have the set of maximal points.
For the value of, the ith maximal layer can be defined as which is set of the maximal points which are defined as. So, which is same as the maximal layer except it has the condition that is included as its new leftmost point.
? if there is a condition that if, then the first k maximal layer of the Q’ will have the set of layer as,
This is same as the value for the maximal layer for Q;
Hence, the Q’ will have a non-empty maximal layer which have the condition of.
c . Here, it is asked to describe a time algorithm for maximal layer for the given set Q which has n-points by moving a sweep line from right to left.
A sweeping line algorithm also known as the plane sweep algorithm is used as an important algorithm in computational geometry. In this algorithm, it is imagined that a line would stop at some points while moving or sweeping across the plane.
Thus, in order to solve the given problem the sweeping of the line is performed in the decreasing order of the coordinate x. Therefore, the sorted set can be denoted by and the initialization will be performed as we sweep left by.
This is done by the inductive method by computing the layers in the correct way till the set and when the set is considered then the layers are and then the y coordinate in the respective layers are the highest y coordinates.
The set is in the layer if there is a condition that and if there is an existence of such k or a new layer is started with the help of a dictionary which is dynamic in nature this is obtained or found in the steps and,
Hence, it can be easily deduced that the entire step will take time.d. Here, it is asked that whether there arise any difficulties or not in case of allowing input points to have the same x- or y-coordinate and also it is asked to suggest a way of resolving or sorting out these problems.
Now, as it is observed that when the input point have the same x and y coordinate then, the case arises when the layer can be obtained by performing the sweeping of the line in the decreasing order of the coordinate x.
So, it can be said that sorting is done on the basis of the values of x coordinate of the points. And, the sorted set is denoted by. For this perform the initialization with the help of sweep operation left by .
This is done by the inductive method by computing the layers in the correct way till the set .and when the set is considered then the layers are and then the y coordinate in the respective layers are the highest y coordinates.
So, when the coordinate are same then the problem of dominance will arise and it can be solved by having the change in coordinate system.
a.
According to the given problem, user have to do the following things:
• There are two sets of points and user have to pair each points from one set with other set points and a line is drawn between them.
• A point may be a part of only one pair.
• These lines should not intersect each other.
Now find the partitioning line:
Suppose and be the two set of n points. Now, two convex hulls and are drawn corresponding to the respective n points of and.
Consider the following diagram:
Now consider the following cases:
Case 1:
There is no convex hull lies completely inside the other convex hull. These lines may be intersecting each other. This implies that a common tangent exists for the given two convex hull.
• All the other points of both the set of points lies to one side of the tangent. So, in this case the tangent is known as the partitioning line.
• The steps taken to find this common tangent is.
• As the steps taken to find the common tangent is and to construct the convex hull is.
Therefore, user can find the partitioning line in steps.
Case 2:
If a convex hull lies completely inside the other convex hull, then through all points in the outer convex hull these exists a partitioning line segment. The proof for the same is as follows:
• A point is taken from the outer convex hull. Now, tangents are drawn from the outer convex hull from the inner convex hull. Total steps taken in this process is.
• Suppose, and be the tangents where, and denotes the points on the insider convex hull. Suppose, the tangent exists in the left of the.
• The number of points in the inner convex hull is less than or equal to the number of points in the outer convex hull to the left of the.
Now, a horizontal axis is chosen and all the points of are sorted to the corresponding to the inner convex hull in the increasing order of the, where, the angle made by the horizontal axis and the line is denoted by the.
It is given that all the, is sorted in an array. Now suppose that are in the sequence.
Now, check if or is a partitioning line segment. If it is not then select a point and check the following for.
At any stage suppose be the interval of the given points in which the partitioning line segment exists.
The recurrence equation for the above is given as:
Therefore, the sorting the point’s takes steps and finding through which the partitioning line passes is steps.
In the above part (a), user find the partitioning line segment in the time complexity of.
• Now, output this pair and these two points are removed from their sets. Now solve the sub problems for the given pair of two sets.
• In the worst case condition, one set pair may each consists points and the other consists 0 points.
Now, consider the following algorithm:
Algorithm:
Suppose be the points consists in and be the points consists in the set.
1. if each set contains one point each then
2. set them as a pair and exit.
3. if each set contains two points in each then
//the pair will be obtained by taking two possibilities
4. if is intersected by then
5. and are the pairs
6. else
7. and are the pairs
8. For the sets and , the convex hulls and will be found respectively.
9. Now, perform checking to decide is one convex hull lies totally inside the other convex
hull.
10. if one convex hull does not exists totally inside the other then
11. obtain their common tangent.
12. Suppose be the tangent then
13. remove and are removed from their respective sets and name them as pairs.
14. Now, go to step 1.
15. Suppose, be. Suppose, completely lies within.
16. then sorting performed on the points of according to their angles.
17. Now obtain the weight for the two points.
18. if any of the obtained weight is zero then
19. that point forms the partitioning line segment with
20. else
21. obtain the weights of the mid-point in the interval of points.
//As given in the previous condition on weight selected the appropriate half of the
//interval to be the new interval.
22. Repeat the step 1 until the weight zero point is found.
23. Name them as the set of pair and then remove from their respective sets then
24. Go to step 1
The above algorithm takes time to pair Ghostbusters and ghosts in such a way that no streams cross.
Picking up Sticks
Consider a set of sticks which are piled up in some configuration. Each stick is defined by its endpoints. Each end point is an ordered triple of and coordinates, which implies that the end point is defined by coordinate. In the given configuration no stick is vertical. Here, a person wants to pick up all the sticks one at a time subjecting to the condition which is as given below:
Condition: A person can pick up a stick only if there is no other stick on top of it.
a. Given two sticks a and b, in order to decide whether a is above, below or unrelated to b these sticks are needed to project to the plane. Based on the projection.
?If the projections of two sticks on plane do not intersect, then it can be said that they are unrelated.
? If the projections of two sticks on plane intersect, then in order to decide which of the stick is above or below, it is required to calculate the coordinate of the sticks corresponding to the intersection point. Stick having larger coordinate value will be above to other.
Procedure:
Input: Two sticks say a and b.
Output: Relation between the given two sticks that is whether a is above, below or unrelated to b.
Sticks
{
if the projection of the a andb on plane do not intersect
Then return unrelated
else
Calculate the point of intersection of the two projections. Let say it is.
Calculate thecoordinate of each stick corresponding to point of intersection. Let’s say it is and
if
then return above;
else
return below;
}
b. Here, it is required to give an efficient algorithm that determines whether it is possible to pick up all the sticks and a legal order in which they will get picked. In order to determine whether it is possible to pick up all the sticks, the sticks will be arranged as Graph. Thus, a directed graph will be constructed having V as the set of vertices and E be the set of the edges. Each vertices of graph represents a stick.
Now, a directed edge between the vertices say a and b in graph is constructed if stick a is above b. Once, graph is constructed in the above manner it is required to run Topological sort algorithm on.
Now, two cases can arise:
? If a cycle is found by Topological sort in then, it is not possible to pick all the sticks.
? Else it is possible to pick all sticks and order of sticks will be the topological sorted order.
Topological sort: A topological sort is an order in which a starting activity depends on the completion of the other activities. That is, order of completed activities play a crucial role in deciding the order of the starting activity. It is a technique or a method of linear ordering of the vertices of a directed graph, such that if there is a directed edge xy from vertex x to vertex y, then x comes before y in the ordering.
This sorting technique is based on the strategy of Depth First Search (DFS). Consider a task that is to be performed. Then, in a directed acyclic graph (dag) the vertices of the directed graph represent the tasks and the edges represent the constraint to be performed on the task, and a topological ordering is the valid sequence of the task.
Topological sorting is only possible if the graph has no directed cycles that is, it is a directed acyclic graph. It is a ordering of vertices in a horizontal line such that all the directed edges go from left to right. In this linear ordering of vertices or nodes if any edge or set of edges direct to left then, it is not a valid solution or ordering. It is also possible for a graph to have more than one valid linear ordering of vertices for a topological sort.
For Example: Consider a directed graph given below:
In the above figure, since there is an edge from B to D and also from C to D. So B and C should be completed before the start of D. Thus, it can be said that A, B, C, D are topologically sorted. Now, also consider the node F, since, there is an edge to F from E and C therefore, C and E should be completed before the start of F. So, Topological order of the above figure or graph is as given below:
ABCDEF
Algorithm:
Input: sticks that is
Output: Determination of the possibility of picking up all the vertices. If possible the legal order in which they should be picked up.
Picking_order()
{
for each stick
construct a vertex for graph;
end for
//Check the relation between two sticks that is whether a stick is above, below
//or unrelated.
for each pair of
Sticks(,)
end for
//graph is constructed
Topological_Sort();
if cycle is found
return not possible;
else
return
Order of sticks produced by topological sort
end if
}
Hence, the algorithm that provides a legal order in which the sticks can be picked up has been described.
The convex hull of the set of point is the smallest convex polygon that contains all the points of set.
A point p is an extreme point of a convex hull if p is not an interior to any line segment connecting two points in the set.
Following diagram represent the example of convex hull.
Input point.
Output =
a.
Consider the following algorithm to compute the convex hull of all n1+n2 point.
1. Compute the vertices with minimum y-axis for both J and K
2. if
3. take the vertices with greater x-coordinate. P(i) for J, Q(m) for K
4. Construct two horizontal lines S1 for J, S2 for K which is parallel with the x- axis
5. else if P(i-1) ,P(i+1), Q(m-1), Q(m+1) lie to the same side of the line
6. user will add a side (P(i),Q(m))
7. if smaller angle is S1 to P(i+1),
8. S1 become the line (P(i), P(i+1)).
9. i=i+1,
10. S2 become the parallel line with S1 through Q(m)
11. else
12. the smaller angle is S2 to Q(m+1), S2 become the line (P(m),P(m+1)
13. m=m+1,
14. S1 become the parallel line with S2 through P(i)
15. MERGE (S1, S2)
16. return S
Analysis of Algorithm:
• In the above algorithm line is break into two parts S1 and S2. If the number of vertices in each part is greater than 1, construct the convex hull of for both S1 and S2.
• MERGE function is used to merge the both individual convex hull into single convex hull so both part will take individually time.
Hence, the total time complexity will be
b.
Consider the following algorithm to compute the convex hull of n point using sparse-hulled distribution.
CONVEX-HULL(S)
//for loop is used to traverse each point of convex hull
1. for i=4 to n
2. j ← Index of the point which is at the right end of CH
// find the upper tangency point
3. u = j
4. while pih4 is not tangent to CH
5. FIND HULL (A, p, r)
6. if (p-r > 3)
7.
8. FIND-HULL (A, p, m)
9. FIND-HULL (A, m+1, r)
10. MERGE-HULL (A,p,q.r)
11. i = j
12. while pihl is not tangent to CH
13. if
14. remove hi from CH
15.
16. INSERT pi in CH between hu and hi
17. return S
Analysis of Complexity:
• In the above algorithm all n points are stored in array A. Then find the convex hull of first point and second point. Finally merge the result.
•
So,
.
Hence, by master theorem .


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Because deskblog is a thin GUI wrapper over the top of the core Pebble code, migrating to a weblog is easy. Just install Pebble weblog and
point it to the directory containing your blog data by setting the blog.dir property in the /WEB-INF/classes/pebble.properties file.
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A film retrospective by Karl Rackwitz (Berlin, Germany, 1998):
-------------- Little Nikita -------------- (USA 1987)
Directed by Richard Benjamin; With River Phoenix, Sidney Poitier, Richard Jenkins, Caroline Kava, Richard Bradford, Richard Lynch
Here we have just another example of two fine actors wasted in a badly written and weakly directed film.
The American teenager Jeff Grant (River Phoenix) applies for the admission into the Air Force Academy. FBI man Roy Parmenter (Sidney Poitier) examines Jeff's particulars and stumbles on contradictory information about Jeff's parents (played by Richard Jenkins and Caroline Kava). At the same time Parmenter works on another case: The former KGB agent Scuba (played by Richard Lynch), who murdered Parmenter's colleague 20 years ago, now commits a series of murders of Russian agents to blackmail the KGB. Then KGB agent Konstantin Karpov (Richard Bradford), who wants to catch Scuba, is seen in Fountain Grove, the Grant family's place of residence. That makes Parmenter conjecture that Jeff's parents are Russian spies. His investigations confirm this suspicion. Jeff doesn't know of the espionage job of his parents. When he is told of it by Roy, with whom he has made friends, he gets thunderstruck. His parents - Russian spies?! Jeff's parents have been living the life of ordinary American people for 20 years. They run a garden nursery in Fountain Grove and use the false family name Grant. Now KGB man Karpov instructs them, who are also potential victims of Scuba, to become active again. They shall help in a surrender of money organized to catch Scuba. The Grants don't want to take part. But Karpov forces them by kidnapping Jeff ...
What may sound like an interesting idea, gets a disappointing film here. "Little Nikita" doesn't nearly exhaust the opportunities of the subject. It convinces neither on the level of Jeff's and his parents' identity problems, nor in its treatment of the Cold War, nor is it a suspenseful spy thriller.
The main problem of the film is its really weak screenplay (by Bo Goldman and John Hill).  The characters of the parents aren't paid enough attention. The Russians are degraded to cartoon characters. In short: The film can't be considered a serious work. Yet it could have become an exciting film. It hasn't. The screenplay is overly constructed and full of improbable incidents leading to a totally unbelievable finale.
Another fatal problem is Richard Benjamin's clumsy direction, especially in the action scenes of the film. An example is the weakly filmed scene, in which Scuba makes a woman be run over by a ship. The directorial flaws in the last 20 minutes of the film are much more serious than in this scene. Benjamin's direction makes the implausible finale even more absurd.
It is due to River Phoenix and Sidney Poitier that "Little Nikita" isn't completely unwatchable. They give the film some interest and a little bit suspense. Phoenix' performance is solid, partially fascinating, although he sometimes tends to overacting here. His interpretation of Jeff Grant hasn't the accuracy of his excellent performances in "Stand by Me" (1986), "Running on Empty" (1987), "Dogfight" (1991) and "My Own Private Idaho" (1991). Sidney Poitier is also quite good in the role of the FBI man Parmenter.
There are some references to the bilateral policy of détente between the Eastern and Western blocs. But to treat this theme convincingly it would have needed a serious dealing instead of clichés.
After all "Little Nikita" is a disappointing film with some entertainment value thanks to the two leads.
To all those who want to see better films starring River Phoenix, I especially recommend the following film in his filmography, Sidney Lumet's masterpiece "Running on Empty" (1987) in which Phoenix gives one of the best performances of his career.
I give "Little Nikita" still a ** rating on the Maltin scale of four stars.
(2.0 out of 4 = Average)
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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--- title: "Overview" type: docs menu: main: name: "Overview" identifier: "deploy-overview" parent: "deploy-top" weight: 1 ---
There are multiple ways to deploy TensorFlow models. In this section we will describe some of the most used ways of deploying those models.
Plumber API: Create a REST API using Plumber to deploy your TensorFlow model. With Plumber you will still depend on having an R runtime which be useful when you want to make the data pre-processing in R.
Shiny: Create a Shiny app that uses a TensorFlow model to generate outputs.
TensorFlow Serving: This is the most performant way of deploying TensorFlow models since it’s based only inn the TensorFlow serving C++ server.
RStudio Connect: RStudio Connect makes it easy to deploy TensorFlow models and uses TensorFlow serving in the backend.
There are many other options to deploy TensorFlow models built with R that are not covered in this section. For example:


{% extends "layout_with_help.html" %} {% block pageTitle %} NRS Diagnostic Prototype {% endblock %} {% block header %} {{ govukHeader() }} {% endblock %} {% block beforeContent %} Save and return to overview {% endblock %} {% block content %}
Read this example and compare it to the added information you have written.
Remember, emails should be written using paragraphs, full sentences and correct spelling.
Dear Sir/Madam
I am writing to apply for the position of Sales Manager at Sony Electronics. After reading the job description and requirements on your job portal, I believe they match with my own experiences and I think that I would make a valuable contribution to your organisation.
Currently, I am employed as a Retail Assistant at a large electronics store in London. Having been in this role for ten years, I have a wide range of experiences in sales and customer communications. I have recently completed an online course to develop my digital skills with the aim of improving my career prospects. I now want to apply these skills in an international company like Sony.


It'd be interesting to find out how the different types of negligent driving behaviors correlate with the total number of collisions. You might want to explore questions like: Which behavior could be a strong predictor of a driver getting involved in crashes again? For states with similiar number of total crashes, which one has the worst drivers in terms of a particular irresponsible behavior?
Use the dropdown menu to select any of the driving behaviors, and see how they relate to the total number of collisions. Hover over the datapoints to view the details. What are your observations on:
Have fun exploring!


Harvard was born into a Navy family in Newport, Rhode Island.
Harvard was born into a Navy family in Newport, Rhode Island.
Harvard was born into a Navy family in Newport, Rhode Island.
"Neque porro quisquam est qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit..."
"Neque porro quisquam est qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit..."
"Neque porro quisquam est qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit..."
"Neque porro quisquam est qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit..."


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The superclass changed from java.lang.Object
to org.apache.hadoop.fs.QuotaUsage
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boolean equals(Object ) |
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String[] getHeaderFields() |
Returns the names of the fields from the summary header. |
String[] getQuotaHeaderFields() |
Returns the names of the fields used in the quota summary. |
long getSnapshotDirectoryCount() |
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long getSnapshotFileCount() |
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long getSnapshotLength() |
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long getSnapshotSpaceConsumed() |
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int hashCode() |
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String toString(boolean, |
Return the string representation of the object in the output format. |
String toString(boolean, |
Return the string representation of the object in the output format. |
String toString(boolean, |
Return the string representation of the object in the output format. |
| Changed Methods | ||
long getQuota() |
Method was locally defined, but is now inherited from QuotaUsage. | Return the directory quota. |
long getSpaceConsumed() |
Method was locally defined, but is now inherited from QuotaUsage. | Return (disk) space consumed. |
long getSpaceQuota() |
Method was locally defined, but is now inherited from QuotaUsage. | Return (disk) space quota. |
long getTypeConsumed(StorageType ) |
Method was locally defined, but is now inherited from QuotaUsage. | Return storage type consumed. |
long getTypeQuota(StorageType ) |
Method was locally defined, but is now inherited from QuotaUsage. | Return storage type quota. |
boolean isTypeConsumedAvailable() |
Method was locally defined, but is now inherited from QuotaUsage. | Return true if any storage type consumption information is available. |
boolean isTypeQuotaSet() |
Method was locally defined, but is now inherited from QuotaUsage. | Return true if any storage type quota has been set. |
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You did not fill out all the fields. Try again!
Traffic in Colorado is at an all-time high! With this phenomena of more people moving to Colorado than the Centennial State can hold, you may be asking yourself: where can I go to see the most traffic at all hours of the day? That's where we here at Centennial Charge are here to get you where you need to be.
The first on our list is a place that will give you the most standstill traffic for a Colorado exit. If people watching and sudden stops are your favorite, then look no further than the eastern path to Denver from cities like Golden, Morrison, and Lakewood. Bonus: merging from three lanes to one is never a sinch, so you may even get cut off 5 times in a row during rush hour! Make sure to drive over here at the 4 o'clock hour to get the full experience.
Peña Boulevard comes in on second on our list for its wonderful length and out-of-the-way nature. As if it wasn't already fun going to an airport located totally outside of Denver city outskirts, then just wait until you hit this long stretch of country road at morning rush hour.
The last on our lsit of the best places to find the busiest traffic in Colorado is the city of Centennia's stretch of C-470. Morning, noon, or night, this road is backed up with accidents galore for your rubbernecking pleasure. For an extra fun time, try going only 10 miles above the speed limit to see how many times you can get tailgated in the right lane!
Are you sure you really want to read this article? Even after all of the effort we just put into telling you where you could go to see the most traffic? Your call.
Death trap, death shmap. If you actually read above instead of going right to the solution, you'd see all of the fun you can have at this wonderful exit!
Okay, that's it. Nothing's "terrifying" about having to stare at some elk crossing the rode as you make your way up to Vail in a snowstorm. But fine. We get it. You really don't appreciate a good traffic jam as much as us. Don't say we didn't warn yah...
You did not fill out all the fields. Try again!


Before Change
// set up (the inverse of) the prior M
if self.use_cov:
X = np.vstack({tuple(row) for row in pairs.reshape(-1, pairs.shape[2])})
prior_inv = np.atleast_2d(np.cov(X, rowvar=False))
else:
prior_inv = np.identity(pairs.shape[2])
diff = pairs[:, 0] - pairs[:, 1]
loss_matrix = (diff.T * y).dot(diff)
After Change
// set up (the inverse of) the prior M
// if the prior is the default (identity), we raise a warning just in case
if self.prior is None:
// TODO:
// replace prior=None by prior="identity" in v0.6.0 and remove the
// warning
msg = ("Warning, no prior was set (`prior=None`). As of version 0.5.0, "
"the default prior will now be set to "
""identity", instead of "covariance". If you still want to use "
"the inverse of the covariance matrix as a prior, "
"set prior="covariance". This warning will disappear in "
"v0.6.0, and `prior` parameter"s default value will be set to "
""identity".")
warnings.warn(msg, ChangedBehaviorWarning)
prior = "identity"
else:
prior = self.prior
_, prior_inv = _initialize_metric_mahalanobis(pairs, prior,
return_inverse=True,
strict_pd=True,
matrix_name="prior")


This site contains three apps that were written by Justin Li as part of this teaching at Occidental College. His experience using these apps is described in the CCSC-SW 2019 paper Explorable Web Apps to Teach AI to Non-Majors. The source code for the apps can be found on GitHub.
| Course Catalog Prerequisite Extraction | App | Assignment |
|---|---|---|
| Pattern-Matching Chatbot | App | Assignment |
| Bayesian Network Calculator | App | Assignment |


--- layout: page title: Teaching Philosophy background: '/img/bg-about.jpg' ---
Education is often modeled as a one size fits all tool to transfer prepackeged knowledge from a book to a brain. I find this model to be totally flawed. Learning is an active process taken on by a student and aided by a teacher. A teacher has the task of getting the student excited to learn, presenting the knowledge in a way that is accessable to the student, and clarifying misunderstandings. This is the process of a guide and a hero embarking to expand the world through the pursuit of truth. Since the primary actor in learning is the individual student, the teacher must consider each individual student and what will allow each student to succeed. The best education cannot be achieved without individual attention being paid to each student.
Let's face it. Math is largly regarded as the most annoying and oftentimes most difficult subject. This is not the fault of students. This is not because some students
"just aren't math people." That fatal phrase has discouraged too many students into giving up on math before they even get started. But it doesn't need to be this way.
Math can be extremely exciting and transformative. With a little bit of math, students can learn how to achieve their financial goals later in life, students can learn
how to solve the most complex problems our world faces, and students can learn how to think clearly and logically when faced with a difficult situation. The biggest problem
with modern math education is that it is filled with rules that have no explanation. Students must memorize formulas, specific problem solving methods, and illogical sequences
of steps in order to solve countless problems without ever learning the most important question: Why?
Why do we want to solve this problem? How can we solve it? Why does
this method of problem solving work? What does the explanation of how this problem is solved tell us about problem solving generally? If students learn how to think about math
clearly, they will be able to understand math on a deeper level and have their lives more fully transformed by math.


Fusion Administration¶
- Before you Configure Google Earth Enterprise Fusion
- Running Fusion on a Machine with Multiple CPUs
- Dynamically adjusting logging levels
- Configure Fusion performance
- Benchmarking outcomes for applying task rules
- Configure font size in the GUI
- Configure fonts for the Text Style dialog
- Customize your Google Maps landing page
- Monitor system processes
- Backing up your data
- Back up Fusion servers
- Manage Fusion disk space
- Relocate your asset root
- Configure your data locations
- Push and publish databases
- Publish databases using disconnected publishing
- Troubleshoot push/publishing issues
- Command reference


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Monthly report, ending on November 1, 2016.
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Given independent and identically distributed observations X(1), ..., X(n) from a density f, provides five methods to perform a multiscale analysis about f as well as the necessary critical values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive correction term Gamma in either method offers another two approaches which are more powerful on small scales and less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically equivalent to the first and was introduced in Rufibach and Walther (2010).
| Version: | 1.0.7 |
| Imports: | stats, utils |
| Published: | 2015-07-03 |
| Author: | Kaspar Rufibach and Guenther Walther |
| Maintainer: | Kaspar Rufibach <kaspar.rufibach at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.kasparrufibach.ch, http://www-stat.stanford.edu/~gwalther |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | modehunt results |
| Reference manual: | modehunt.pdf |
| Package source: | modehunt_1.0.7.tar.gz |
| Windows binaries: | r-devel: modehunt_1.0.7.zip, r-release: modehunt_1.0.7.zip, r-oldrel: modehunt_1.0.7.zip |
| OS X binaries: | r-release: modehunt_1.0.7.tgz, r-oldrel: modehunt_1.0.7.tgz |
| Old sources: | modehunt archive |
Please use the canonical form https://CRAN.R-project.org/package=modehunt to link to this page.


An even better, faster and more stable Trello experience powered by community feedback!
Yes - there were some issues when loading the extension. Sometimes cards would dissapear, sometimes the Pro4Trello menu wouldn't appear. But that's all in the past. And it all just works so much faster now
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Some fine CSS tuning for your Trello boards, streight from the community. You can now make your board very classy and compact:
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#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2019, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
''' Various kinds of layout components.
'''
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
log = logging.getLogger(__name__)
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Standard library imports
# External imports
# Bokeh imports
from ..core.enums import Align, SizingMode, SizingPolicy, Location
from ..core.has_props import abstract
from ..core.properties import (Bool, Auto, Enum, Int, NonNegativeInt, Float,
Instance, List, Seq, Tuple, Dict, String, Either, Struct, Color)
from ..core.validation import warning, error
from ..core.validation.warnings import (BOTH_CHILD_AND_ROOT, EMPTY_LAYOUT,
FIXED_SIZING_MODE, FIXED_WIDTH_POLICY, FIXED_HEIGHT_POLICY)
from ..core.validation.errors import MIN_PREFERRED_MAX_WIDTH, MIN_PREFERRED_MAX_HEIGHT
from ..model import Model
from .callbacks import Callback
#-----------------------------------------------------------------------------
# Globals and constants
#-----------------------------------------------------------------------------
__all__ = (
'Box',
'Column',
'GridBox',
'HTMLBox',
'LayoutDOM',
'Row',
'Spacer',
'WidgetBox',
)
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
[docs]@abstract
class LayoutDOM(Model):
""" The base class for layoutable components.
"""
disabled = Bool(False, help="""
Whether the widget will be disabled when rendered.
If ``True``, the widget will be greyed-out and not responsive to UI events.
""")
visible = Bool(True, help="""
Whether the component will be visible and a part of a layout.
""")
width = NonNegativeInt(default=None, help="""
The width of the component (in pixels).
This can be either fixed or preferred width, depending on width sizing policy.
""")
height = NonNegativeInt(default=None, help="""
The height of the component (in pixels).
This can be either fixed or preferred height, depending on height sizing policy.
""")
min_width = NonNegativeInt(default=None, help="""
Minimal width of the component (in pixels) if width is adjustable.
""")
min_height = NonNegativeInt(default=None, help="""
Minimal height of the component (in pixels) if height is adjustable.
""")
max_width = NonNegativeInt(default=None, help="""
Minimal width of the component (in pixels) if width is adjustable.
""")
max_height = NonNegativeInt(default=None, help="""
Minimal height of the component (in pixels) if height is adjustable.
""")
margin = Tuple(Int, Int, Int, Int, default=(0, 0, 0, 0), help="""
Allows to create additional space around the component.
""").accepts(Tuple(Int, Int), lambda v_h: (v_h[0], v_h[1], v_h[0], v_h[1])) \
.accepts(Int, lambda m: (m, m, m, m))
width_policy = Either(Auto, Enum(SizingPolicy), default="auto", help="""
Describes how the component should maintain its width.
``"auto"``
Use component's preferred sizing policy.
``"fixed"``
Use exactly ``width`` pixels. Component will overflow if it can't fit in the
available horizontal space.
``"fit"``
Use component's preferred width (if set) and allow it to fit into the available
horizontal space within the minimum and maximum width bounds (if set). Component's
width neither will be aggressively minimized nor maximized.
``"min"``
Use as little horizontal space as possible, not less than the minimum width (if set).
The starting point is the preferred width (if set). The width of the component may
shrink or grow depending on the parent layout, aspect management and other factors.
``"max"``
Use as much horizontal space as possible, not more than the maximum width (if set).
The starting point is the preferred width (if set). The width of the component may
shrink or grow depending on the parent layout, aspect management and other factors.
.. note::
This is an experimental feature and may change in future. Use it at your
own discretion. Prefer using ``sizing_mode`` if this level of control isn't
strictly necessary.
""")
height_policy = Either(Auto, Enum(SizingPolicy), default="auto", help="""
Describes how the component should maintain its height.
``"auto"``
Use component's preferred sizing policy.
``"fixed"``
Use exactly ``height`` pixels. Component will overflow if it can't fit in the
available vertical space.
``"fit"``
Use component's preferred height (if set) and allow to fit into the available
vertical space withing the minimum and maximum height bounds (if set). Component's
height neither will be aggressively minimized nor maximized.
``"min"``
Use as little vertical space as possible, not less than the minimum height (if set).
The starting point is the preferred height (if set). The height of the component may
shrink or grow depending on the parent layout, aspect management and other factors.
``"max"``
Use as much vertical space as possible, not more than the maximum height (if set).
The starting point is the preferred height (if set). The height of the component may
shrink or grow depending on the parent layout, aspect management and other factors.
.. note::
This is an experimental feature and may change in future. Use it at your
own discretion. Prefer using ``sizing_mode`` if this level of control isn't
strictly necessary.
""")
aspect_ratio = Either(Enum("auto"), Float, default=None, help="""
Describes the proportional relationship between component's width and height.
This works if any of component's dimensions are flexible in size. If set to
a number, ``width / height = aspect_ratio`` relationship will be maintained.
Otherwise, if set to ``"auto"``, component's preferred width and height will
be used to determine the aspect (if not set, no aspect will be preserved).
""")
sizing_mode = Enum(SizingMode, default=None, help="""
How the component should size itself.
This is a high-level setting for maintaining width and height of the component. To
gain more fine grained control over sizing, use ``width_policy``, ``height_policy``
and ``aspect_ratio`` instead (those take precedence over ``sizing_mode``).
Possible scenarios:
``"fixed"``
Component is not responsive. It will retain its original width and height
regardless of any subsequent browser window resize events.
``"stretch_width"``
Component will responsively resize to stretch to the available width, without
maintaining any aspect ratio. The height of the component depends on the type
of the component and may be fixed or fit to component's contents.
``"stretch_height"``
Component will responsively resize to stretch to the available height, without
maintaining any aspect ratio. The width of the component depends on the type
of the component and may be fixed or fit to component's contents.
``"stretch_both"``
Component is completely responsive, independently in width and height, and
will occupy all the available horizontal and vertical space, even if this
changes the aspect ratio of the component.
``"scale_width"``
Component will responsively resize to stretch to the available width, while
maintaining the original or provided aspect ratio.
``"scale_height"``
Component will responsively resize to stretch to the available height, while
maintaining the original or provided aspect ratio.
``"scale_both"``
Component will responsively resize to both the available width and height, while
maintaining the original or provided aspect ratio.
""")
align = Either(Enum(Align), Tuple(Enum(Align), Enum(Align)), default="start", help="""
The alignment point within the parent container.
This property is useful only if this component is a child element of a layout
(e.g. a grid). Self alignment can be overridden by the parent container (e.g.
grid track align).
""")
background = Color(default=None, help="""
Background color of the component.
""")
# List in order for in-place changes to trigger changes, ref: https://github.com/bokeh/bokeh/issues/6841
css_classes = List(String, help="""
A list of CSS class names to add to this DOM element. Note: the class names are
simply added as-is, no other guarantees are provided.
It is also permissible to assign from tuples, however these are adapted -- the
property will always contain a list.
""").accepts(Seq(String), lambda x: list(x))
@warning(FIXED_SIZING_MODE)
def _check_fixed_sizing_mode(self):
if self.sizing_mode == "fixed" and (self.width is None or self.height is None):
return str(self)
@warning(FIXED_WIDTH_POLICY)
def _check_fixed_width_policy(self):
if self.width_policy == "fixed" and self.width is None:
return str(self)
@warning(FIXED_HEIGHT_POLICY)
def _check_fixed_height_policy(self):
if self.height_policy == "fixed" and self.height is None:
return str(self)
@error(MIN_PREFERRED_MAX_WIDTH)
def _min_preferred_max_width(self):
min_width = self.min_width if self.min_width is not None else 0
width = self.width if self.width is not None else min_width
max_width = self.max_width if self.max_width is not None else width
if not (min_width <= width <= max_width):
return str(self)
@error(MIN_PREFERRED_MAX_HEIGHT)
def _min_preferred_max_height(self):
min_height = self.min_height if self.min_height is not None else 0
height = self.height if self.height is not None else min_height
max_height = self.max_height if self.max_height is not None else height
if not (min_height <= height <= max_height):
return str(self)
[docs]@abstract
class HTMLBox(LayoutDOM):
''' A component which size is determined by its HTML content.
'''
[docs]class Spacer(LayoutDOM):
''' A container for space used to fill an empty spot in a row or column.
'''
QuickTrackSizing = Either(Enum("auto", "min", "fit", "max"), Int)
TrackAlign = Either(Auto, Enum(Align))
RowSizing = Either(
QuickTrackSizing,
Struct(policy=Enum("auto", "min"), align=TrackAlign),
Struct(policy=Enum("fixed"), height=Int, align=TrackAlign),
Struct(policy=Enum("fit", "max"), flex=Float, align=TrackAlign))
ColSizing = Either(
QuickTrackSizing,
Struct(policy=Enum("auto", "min"), align=TrackAlign),
Struct(policy=Enum("fixed"), width=Int, align=TrackAlign),
Struct(policy=Enum("fit", "max"), flex=Float, align=TrackAlign))
IntOrString = Either(Int, String) # XXX: work around issue #8166
[docs]class GridBox(LayoutDOM):
children = List(Either(
Tuple(Instance(LayoutDOM), Int, Int),
Tuple(Instance(LayoutDOM), Int, Int, Int, Int)), default=[], help="""
A list of children with their associated position in the grid (row, column).
""")
rows = Either(QuickTrackSizing, Dict(IntOrString, RowSizing), default="auto", help="""
Describes how the grid should maintain its rows' heights.
.. note::
This is an experimental feature and may change in future. Use it at your
own discretion.
""")
cols = Either(QuickTrackSizing, Dict(IntOrString, ColSizing), default="auto", help="""
Describes how the grid should maintain its columns' widths.
.. note::
This is an experimental feature and may change in future. Use it at your
own discretion.
""")
spacing = Either(Int, Tuple(Int, Int), default=0, help="""
The gap between children (in pixels).
Either a number, if spacing is the same for both dimensions, or a pair
of numbers indicating spacing in the vertical and horizontal dimensions
respectively.
""")
[docs]@abstract
class Box(LayoutDOM):
''' Abstract base class for Row and Column. Do not use directly.
'''
def __init__(self, *args, **kwargs):
if len(args) > 0 and "children" in kwargs:
raise ValueError("'children' keyword cannot be used with positional arguments")
elif len(args) > 0:
kwargs["children"] = list(args)
super(Box, self).__init__(**kwargs)
@warning(EMPTY_LAYOUT)
def _check_empty_layout(self):
from itertools import chain
if not list(chain(self.children)):
return str(self)
@warning(BOTH_CHILD_AND_ROOT)
def _check_child_is_also_root(self):
problems = []
for c in self.children:
if c.document is not None and c in c.document.roots:
problems.append(str(c))
if problems:
return ", ".join(problems)
else:
return None
children = List(Instance(LayoutDOM), help="""
The list of children, which can be other components including plots, rows, columns, and widgets.
""")
spacing = Int(default=0, help="""
The gap between children (in pixels).
""")
[docs]class Row(Box):
''' Lay out child components in a single horizontal row.
Children can be specified as positional arguments, as a single argument
that is a sequence, or using the ``children`` keyword argument.
'''
cols = Either(QuickTrackSizing, Dict(IntOrString, ColSizing), default="auto", help="""
Describes how the component should maintain its columns' widths.
.. note::
This is an experimental feature and may change in future.
Children can be specified as positional arguments, as a single argument
that is a sequence, or using the ``children`` keyword argument.
'''
rows = Either(QuickTrackSizing, Dict(IntOrString, RowSizing), default="auto", help="""
Describes how the component should maintain its rows' heights.
.. note::
This is an experimental feature and may change in future. Use it at your
own discretion.
""")
class Panel(Model):
''' A single-widget container with title bar and controls.
'''
title = String(default="", help="""
The text title of the panel.
""")
child = Instance(LayoutDOM, help="""
The child widget. If you need more children, use a layout widget, e.g. a ``Column``.
""")
closable = Bool(False, help="""
Whether this panel is closable or not. If True, an "x" button will appear.
Closing a panel is equivalent to removing it from its parent container (e.g. tabs).
""")
class Tabs(LayoutDOM):
''' A panel widget with navigation tabs.
'''
__example__ = "sphinx/source/docs/user_guide/examples/interaction_tab_panes.py"
tabs = List(Instance(Panel), help="""
The list of child panel widgets.
""").accepts(List(Tuple(String, Instance(LayoutDOM))),
lambda items: [ Panel(title=title, child=child) for (title, child) in items ])
tabs_location = Enum(Location, default="above", help="""
The location of the buttons that activate tabs.
""")
active = Int(0, help="""
The index of the active tab.
""")
callback = Instance(Callback, help="""
A callback to run in the browser whenever the button is activated.
""")
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------


--- layout: page title: Games permalink: /games/ ---
{{ game.description }}
{{ prototype.description }}


{% extends 'base.html'%} {% import 'macros.html' as macro%} {%block content%}
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