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• Inequality joins, Asynchronous transformations and Lookups : SSIS

  - by jamiet

  It is pretty much accepted by SQL Server Integration Services (SSIS) developers that synchronous transformations are generally quicker than asynchronous transformations (for a description of synchronous and asynchronous transformations go read Asynchronous and synchronous data flow components). Notice I said “generally” and not “always”; there are circumstances where using asynchronous transformations can be beneficial and in this blog post I’ll demonstrate such a scenario, one that is pretty common when building data warehouses. Imagine I have a [Customer] dimension table that manages information about all of my customers as a slowly-changing dimension. If that is a type 2 slowly changing dimension then you will likely have multiple rows per customer in that table. Furthermore you might also have datetime fields that indicate the effective time period of each member record. Here is such a table that contains data for four dimension members {Terry, Max, Henry, Horace}: Notice that we have multiple records per customer and that the [SCDStartDate] of a record is equivalent to the [SCDEndDate] of the record that preceded it (if there was one). (Note that I am on record as saying I am not a fan of this technique of storing an [SCDEndDate] but for the purposes of clarity I have included it here.) Anyway, the idea here is that we will have some incoming data containing [CustomerName] & [EffectiveDate] and we need to use those values to lookup [Customer].[CustomerId]. The logic will be: Lookup a [CustomerId] WHERE [CustomerName]=[CustomerName] AND [SCDStartDate] <= [EffectiveDate] AND [EffectiveDate] <= [SCDEndDate] The conventional approach to this would be to use a full cached lookup but that isn’t an option here because we are using inequality conditions. The obvious next step then is to use a non-cached lookup which enables us to change the SQL statement to use inequality operators: Let’s take a look at the dataflow: Notice these are all synchronous components. This approach works just fine however it does have the limitation that it has to issue a SQL statement against your lookup set for every row thus we can expect the execution time of our dataflow to increase linearly in line with the number of rows in our dataflow; that’s not good. OK, that’s the obvious method. Let’s now look at a different way of achieving this using an asynchronous Merge Join transform coupled with a Conditional Split. I’ve shown it post-execution so that I can include the row counts which help to illustrate what is going on here: Notice that there are more rows output from our Merge Join component than on the input. That is because we are joining on [CustomerName] and, as we know, we have multiple records per [CustomerName] in our lookup set. Notice also that there are two asynchronous components in here (the Sort and the Merge Join). I have embedded a video below that compares the execution times for each of these two methods. The video is just over 8minutes long. View on Vimeo  For those that can’t be bothered watching the video I’ll tell you the results here. The dataflow that used the Lookup transform took 36 seconds whereas the dataflow that used the Merge Join took less than two seconds. An illustration in case it is needed: Pretty conclusive proof that in some scenarios it may be quicker to use an asynchronous component than a synchronous one. Your mileage may of course vary. The scenario outlined here is analogous to performance tuning procedural SQL that uses cursors. It is common to eliminate cursors by converting them to set-based operations and that is effectively what we have done here. Our non-cached lookup is performing a discrete operation for every single row of data, exactly like a cursor does. By eliminating this cursor-in-disguise we have dramatically sped up our dataflow. I hope all of that proves useful. You can download the package that I demonstrated in the video from my SkyDrive at http://cid-550f681dad532637.skydrive.live.com/self.aspx/Public/BlogShare/20100514/20100514%20Lookups%20and%20Merge%20Joins.zip Comments are welcome as always. @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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• Appengine filter inequality and ordering fails

  - by davezor

  I think I'm overlooking something simple here, I can't imagine this is impossible to do. I want to filter by a datetime attribute and then order the result by a ranking integer attribute. When I try to do this: query.filter("submitted >=" thisweek).order("ranking") I get the following: BadArgumentError: First ordering property must be the same as inequality filter property, if specified for this query; received ranking, expected submitted Huh? What am I missing? Thanks.

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• Multiple inequality conditions (range queries) in NoSQL

  - by pableu

  Hi, I have an application where I'd like to use a NoSQL database, but I still want to do range queries over two different properties, for example select all entries between times T1 and T2 where the noiselevel is smaller than X. On the other hand, I would like to use a NoSQL/Key-Value store because my data is very sparse and diverse, and I do not want to create new tables for every new datatype that I might come across. I know that you cannot use multiple inequality filters for the Google Datastore (source). I also know that this feature is coming (according to this). I know that this is also not possible in CouchDB (source). I think I also more or less understand why this is the case. Now, this makes me wonder.. Is that the case with all NoSQL databases? Can other NoSQL systems make range queries over two different properties? How about, for example, Mongo DB? I've looked in the Documentation, but the only thing I've found was the following snippet in their docu: Note that any of the operators on this page can be combined in the same query document. For example, to find all document where j is not equal to 3 and k is greater than 10, you'd query like so: db.things.find({j: {$ne: 3}, k: {$gt: 10} }); So they use greater-than and not-equal on two different properties. They don't say anything about two inequalities ;-) Any input and enlightenment is welcome :-)

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• Chained inequality notation in programming languages

  - by Davorin

  Is there a programming language that supports chained notation a < b < c to be used instead of a < b and b < c in conditional statements? Example: if ( 2 < x < 5 ) if ( 2 < x && x < 5 ) First statementlooks better to me, it's easier to understand and the compiler could use transitivity property to warn about mistakes (e.g. 5 < x < 2 would give a warning).

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• Proving that a function f(n) belongs to a Big-Theta(g(n))

  - by PLS

  Its a exercise that ask to indicate the class Big-Theta(g(n)) the functions belongs to and to prove the assertion. In this case f(n) = (n^2+1)^10 By definition f(n) E Big-Theta(g(n)) <= c1*g(n) < f(n) < c2*g(n), where c1 and c2 are two constants. I know that for this specific f(n) the Big-Theta is g(n^20) but I don't know who to prove it properly. I guess I need to manipulate this inequality but I don't know how

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• !(ReferenceEquals()) vs != in Entity Framework 4

  - by Eric J.

  Unless a class specifically overrides the behavior defined for Object, ReferenceEquals and == do the same thing... compare references. In property setters, I have commonly used the pattern private MyType myProperty; public MyType MyProperty { set { if (myProperty != value) { myProperty = value; // Do stuff like NotifyPropertyChanged } } } However, in code generated by Entity Framework, the if statement is replaced by if (!ReferenceEquals(myProperty, value)) Using ReferenceEquals is more explicit (as I guess not all C# programmers know that == does the same thing if not overridden). Is there any difference that's escaping me between the two if-variants? Are they perhaps accounting for the possibility that POCO designers may have overridden ==? In short, if I have not overridden ==, am I save using != instead of ReferencEquals()?

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• Entity Framework 4: C# !(ReferenceEquals()) vs !=

  - by Eric J.

  Unless a class specifically overrides the behavior defined for Object, ReferenceEquals and == do the same thing... compare references. In property setters, I have commonly used the pattern private MyType myProperty; public MyType MyProperty { set { if (myProperty != value) { myProperty = value; // Do stuff like NotifyPropertyChanged } } } However, in code generated by Entity Framework, the if statement is replaced by if (!ReferenceEquals(myProperty, value)) Using ReferenceEquals is more explicit (as I guess not all C# programmers know that == does the same thing if not overridden). Is there any difference that's escaping me between the two if-variants? Are they perhaps accounting for the possibility that POCO designers may have overridden ==? In short, if I have not overridden ==, am I save using != instead of ReferencEquals()?

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• python- scipy optimization

  - by pear

  In scipy fmin_slsqp (Sequential Least Squares Quadratic Programming), I tried reading the code 'slsqp.py' provided with the scipy package, to find what are the criteria to get the exit_modes 0? I cannot find which statements in the code produce this exit mode? Please help me 'slsqp.py' code as follows, exit_modes = { -1 : "Gradient evaluation required (g & a)", 0 : "Optimization terminated successfully.", 1 : "Function evaluation required (f & c)", 2 : "More equality constraints than independent variables", 3 : "More than 3*n iterations in LSQ subproblem", 4 : "Inequality constraints incompatible", 5 : "Singular matrix E in LSQ subproblem", 6 : "Singular matrix C in LSQ subproblem", 7 : "Rank-deficient equality constraint subproblem HFTI", 8 : "Positive directional derivative for linesearch", 9 : "Iteration limit exceeded" } def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_eqcons=None, fprime_ieqcons=None, args = (), iter = 100, acc = 1.0E-6, iprint = 1, full_output = 0, epsilon = _epsilon ): # Now do a lot of function wrapping # Wrap func feval, func = wrap_function(func, args) # Wrap fprime, if provided, or approx_fprime if not if fprime: geval, fprime = wrap_function(fprime,args) else: geval, fprime = wrap_function(approx_fprime,(func,epsilon)) if f_eqcons: # Equality constraints provided via f_eqcons ceval, f_eqcons = wrap_function(f_eqcons,args) if fprime_eqcons: # Wrap fprime_eqcons geval, fprime_eqcons = wrap_function(fprime_eqcons,args) else: # Wrap approx_jacobian geval, fprime_eqcons = wrap_function(approx_jacobian, (f_eqcons,epsilon)) else: # Equality constraints provided via eqcons[] eqcons_prime = [] for i in range(len(eqcons)): eqcons_prime.append(None) if eqcons[i]: # Wrap eqcons and eqcons_prime ceval, eqcons[i] = wrap_function(eqcons[i],args) geval, eqcons_prime[i] = wrap_function(approx_fprime, (eqcons[i],epsilon)) if f_ieqcons: # Inequality constraints provided via f_ieqcons ceval, f_ieqcons = wrap_function(f_ieqcons,args) if fprime_ieqcons: # Wrap fprime_ieqcons geval, fprime_ieqcons = wrap_function(fprime_ieqcons,args) else: # Wrap approx_jacobian geval, fprime_ieqcons = wrap_function(approx_jacobian, (f_ieqcons,epsilon)) else: # Inequality constraints provided via ieqcons[] ieqcons_prime = [] for i in range(len(ieqcons)): ieqcons_prime.append(None) if ieqcons[i]: # Wrap ieqcons and ieqcons_prime ceval, ieqcons[i] = wrap_function(ieqcons[i],args) geval, ieqcons_prime[i] = wrap_function(approx_fprime, (ieqcons[i],epsilon)) # Transform x0 into an array. x = asfarray(x0).flatten() # Set the parameters that SLSQP will need # meq = The number of equality constraints if f_eqcons: meq = len(f_eqcons(x)) else: meq = len(eqcons) if f_ieqcons: mieq = len(f_ieqcons(x)) else: mieq = len(ieqcons) # m = The total number of constraints m = meq + mieq # la = The number of constraints, or 1 if there are no constraints la = array([1,m]).max() # n = The number of independent variables n = len(x) # Define the workspaces for SLSQP n1 = n+1 mineq = m - meq + n1 + n1 len_w = (3*n1+m)*(n1+1)+(n1-meq+1)*(mineq+2) + 2*mineq+(n1+mineq)*(n1-meq) \ + 2*meq + n1 +(n+1)*n/2 + 2*m + 3*n + 3*n1 + 1 len_jw = mineq w = zeros(len_w) jw = zeros(len_jw) # Decompose bounds into xl and xu if len(bounds) == 0: bounds = [(-1.0E12, 1.0E12) for i in range(n)] elif len(bounds) != n: raise IndexError, \ 'SLSQP Error: If bounds is specified, len(bounds) == len(x0)' else: for i in range(len(bounds)): if bounds[i][0] > bounds[i][1]: raise ValueError, \ 'SLSQP Error: lb > ub in bounds[' + str(i) +'] ' + str(bounds[4]) xl = array( [ b[0] for b in bounds ] ) xu = array( [ b[1] for b in bounds ] ) # Initialize the iteration counter and the mode value mode = array(0,int) acc = array(acc,float) majiter = array(iter,int) majiter_prev = 0 # Print the header if iprint >= 2 if iprint >= 2: print "%5s %5s %16s %16s" % ("NIT","FC","OBJFUN","GNORM") while 1: if mode == 0 or mode == 1: # objective and constraint evaluation requird # Compute objective function fx = func(x) # Compute the constraints if f_eqcons: c_eq = f_eqcons(x) else: c_eq = array([ eqcons[i](x) for i in range(meq) ]) if f_ieqcons: c_ieq = f_ieqcons(x) else: c_ieq = array([ ieqcons[i](x) for i in range(len(ieqcons)) ]) # Now combine c_eq and c_ieq into a single matrix if m == 0: # no constraints c = zeros([la]) else: # constraints exist if meq > 0 and mieq == 0: # only equality constraints c = c_eq if meq == 0 and mieq > 0: # only inequality constraints c = c_ieq if meq > 0 and mieq > 0: # both equality and inequality constraints exist c = append(c_eq, c_ieq) if mode == 0 or mode == -1: # gradient evaluation required # Compute the derivatives of the objective function # For some reason SLSQP wants g dimensioned to n+1 g = append(fprime(x),0.0) # Compute the normals of the constraints if fprime_eqcons: a_eq = fprime_eqcons(x) else: a_eq = zeros([meq,n]) for i in range(meq): a_eq[i] = eqcons_prime[i](x) if fprime_ieqcons: a_ieq = fprime_ieqcons(x) else: a_ieq = zeros([mieq,n]) for i in range(mieq): a_ieq[i] = ieqcons_prime[i](x) # Now combine a_eq and a_ieq into a single a matrix if m == 0: # no constraints a = zeros([la,n]) elif meq > 0 and mieq == 0: # only equality constraints a = a_eq elif meq == 0 and mieq > 0: # only inequality constraints a = a_ieq elif meq > 0 and mieq > 0: # both equality and inequality constraints exist a = vstack((a_eq,a_ieq)) a = concatenate((a,zeros([la,1])),1) # Call SLSQP slsqp(m, meq, x, xl, xu, fx, c, g, a, acc, majiter, mode, w, jw) # Print the status of the current iterate if iprint > 2 and the # major iteration has incremented if iprint >= 2 and majiter > majiter_prev: print "%5i %5i % 16.6E % 16.6E" % (majiter,feval[0], fx,linalg.norm(g)) # If exit mode is not -1 or 1, slsqp has completed if abs(mode) != 1: break majiter_prev = int(majiter) # Optimization loop complete. Print status if requested if iprint >= 1: print exit_modes[int(mode)] + " (Exit mode " + str(mode) + ')' print " Current function value:", fx print " Iterations:", majiter print " Function evaluations:", feval[0] print " Gradient evaluations:", geval[0] if not full_output: return x else: return [list(x), float(fx), int(majiter), int(mode), exit_modes[int(mode)] ]

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• July, the 31 Days of SQL Server DMO’s – Day 25 (sys.dm_db_missing_index_details)

  - by Tamarick Hill

  The sys.dm_db_missing_index_details Dynamic Management View is used to return information about missing indexes on your SQL Server instances. These indexes are ones that the optimizer has identified as indexes it would like to use but did not have. You may also see these same indexes indicated in other tools such as query execution plans or the Database tuning advisor. Let’s execute this DMV so we can review the information it provides us. I do not have any missing index information for my AdventureWorks2012 database, but for the purposes of illustrating the result set of this DMV, I will present the results from my msdb database. SELECT * FROM sys.dm_db_missing_index_details The first column presented is the index_handle which uniquely identifies a particular missing index. The next two columns represent the database_id and the object_id for the particular table in question. Next is the ‘equality_columns’ column which gives you a list of columns (comma separated) that would be beneficial to the optimizer for equality operations. By equality operation I mean for any queries that would use a filter or join condition such as WHERE A = B. The next column, ‘inequality_columns’, gives you a comma separated list of columns that would be beneficial to the optimizer for inequality operations. An inequality operation is anything other than A = B. For example, “WHERE A != B”, “WHERE A > B”, “WHERE A < B”, and “WHERE A <> B” would all qualify as inequality. Next is the ‘included_columns’ column which list all columns that would be beneficial to the optimizer for purposes of providing a covering index and preventing key/bookmark lookups. Lastly is the ‘statement’ column which lists the name of the table where the index is missing. This DMV can help you identify potential indexes that could be added to improve the performance of your system. However, I will advise you not to just take the output of this DMV and create an index for everything you see. Everything listed here should be analyzed and then tested on a Development or Test system before implementing into a Production environment. For more information on this DMV, please see the below Books Online link: http://msdn.microsoft.com/en-us/library/ms345434.aspx Follow me on Twitter @PrimeTimeDBA

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• AppEngine JDO-QL : Problem with multiple AND and OR in query

  - by KlasE

  Hi, I'm working with App Engine(Java/JDO) and are trying to do some querying with lists. So I have the following class: @PersistenceCapable(identityType = IdentityType.APPLICATION, detachable="true") public class MyEntity { @PrimaryKey @Persistent(valueStrategy = IdGeneratorStrategy.IDENTITY) Key key; @Persistent List<String> tags = new ArrayList<String>(); @Persistent String otherVar; } The following JDO-QL works for me: ( tags.contains('a') || tags.contains('b') || tags.contains('c') || tags.contains('d') ) && otherVar > 'a' && otherVar < 'z' This seem to return all results where tags has one or more strings with one or more of the given values and together with the inequality search on otherVar But the following do not work: (tags.contains('a') || tags.contains('_a')) && (tags.contains('b') || tags.contains('_b')) && otherVar > 'a' && otherVar < 'z' In this case I want all hits where there is at least one a (either a or _a) and one b (either b or _b) together with the inequality search as before. But the problem is that I also get back the results where there is a but no b, which is not what I want. Maybe I have missed something obvious, or done a coding error, or possibly there is a restriction on how you can write these queries in appengine, so any tips or help would be very welcome. Regards Klas

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• What algorithms compute directions from point A to point B on a map?

  - by A. Rex

  How do map providers (such as Google or Yahoo! Maps) suggest directions? I mean, they probably have real-world data in some form, certainly including distances but also perhaps things like driving speeds, presence of sidewalks, train schedules, etc. But suppose the data were in a simpler format, say a very large directed graph with edge weights reflecting distances. I want to be able to quickly compute directions from one arbitrary point to another. Sometimes these points will be close together (within one city) while sometimes they will be far apart (cross-country). Graph algorithms like Dijkstra's algorithm will not work because the graph is enormous. Luckily, heuristic algorithms like A* will probably work. However, our data is very structured, and perhaps some kind of tiered approach might work? (For example, store precomputed directions between certain "key" points far apart, as well as some local directions. Then directions for two far-away points will involve local directions to a key points, global directions to another key point, and then local directions again.) What algorithms are actually used in practice? PS. This question was motivated by finding quirks in online mapping directions. Contrary to the triangle inequality, sometimes Google Maps thinks that X-Z takes longer and is farther than using an intermediate point as in X-Y-Z. But maybe their walking directions optimize for another parameter, too? PPS. Here's another violation of the triangle inequality that suggests (to me) that they use some kind of tiered approach: X-Z versus X-Y-Z. The former seems to use prominent Boulevard de Sebastopol even though it's slightly out of the way. (Edit: this example doesn't work anymore, but did at the time of the original post. The one above still works as of early November 2009.)

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• Deferred rendering with VSM - Scaling light depth loses moments

  - by user1423893

  I'm calculating my shadow term using a VSM method. This works correctly when using forward rendered lights but fails with deferred lights. // Shadow term (1 = no shadow) float shadow = 1; // [Light Space -> Shadow Map Space] // Transform the surface into light space and project // NB: Could be done in the vertex shader, but doing it here keeps the // "light shader" abstraction and doesn't limit the number of shadowed lights float4x4 LightViewProjection = mul(LightView, LightProjection); float4 surf_tex = mul(position, LightViewProjection); // Re-homogenize // 'w' component is not used in later calculations so no need to homogenize (it will equal '1' if homogenized) surf_tex.xyz /= surf_tex.w; // Rescale viewport to be [0,1] (texture coordinate system) float2 shadow_tex; shadow_tex.x = surf_tex.x * 0.5f + 0.5f; shadow_tex.y = -surf_tex.y * 0.5f + 0.5f; // Half texel offset //shadow_tex += (0.5 / 512); // Scaled distance to light (instead of 'surf_tex.z') float rescaled_dist_to_light = dist_to_light / LightAttenuation.y; //float rescaled_dist_to_light = surf_tex.z; // [Variance Shadow Map Depth Calculation] // No filtering float2 moments = tex2D(ShadowSampler, shadow_tex).xy; // Flip the moments values to bring them back to their original values moments.x = 1.0 - moments.x; moments.y = 1.0 - moments.y; // Compute variance float E_x2 = moments.y; float Ex_2 = moments.x * moments.x; float variance = E_x2 - Ex_2; variance = max(variance, Bias.y); // Surface is fully lit if the current pixel is before the light occluder (lit_factor == 1) // One-tailed inequality valid if float lit_factor = (rescaled_dist_to_light <= moments.x - Bias.x); // Compute probabilistic upper bound (mean distance) float m_d = moments.x - rescaled_dist_to_light; // Chebychev's inequality float p = variance / (variance + m_d * m_d); p = ReduceLightBleeding(p, Bias.z); // Adjust the light color based on the shadow attenuation shadow *= max(lit_factor, p); This is what I know for certain so far: The lighting is correct if I do not try and calculate the shadow term. (No shadows) The shadow term is correct when calculated using forward rendered lighting. (VSM works with forward rendered lights) With the current rescaled light distance (lightAttenuation.y is the far plane value): float rescaled_dist_to_light = dist_to_light / LightAttenuation.y; The light is correct and the shadow appears to be zoomed in and misses the blurring: When I do not rescale the light and use the homogenized 'surf_tex': float rescaled_dist_to_light = surf_tex.z; the shadows are blurred correctly but the lighting is incorrect and the cube model is no longer lit Why is scaling by the far plane value (LightAttenuation.y) zooming in too far? The only other factor involved is my world pixel position, which is calculated as follows: // [Position] float4 position; // [Screen Position] position.xy = input.PositionClone.xy; // Use 'x' and 'y' components already homogenized for uv coordinates above position.z = tex2D(DepthSampler, texCoord).r; // No need to homogenize 'z' component position.z = 1.0 - position.z; position.w = 1.0; // 1.0 = position.w / position.w // [World Position] position = mul(position, CameraViewProjectionInverse); // Re-homogenize position (xyz AND w, otherwise shadows will bend when camera is close) position.xyz /= position.w; position.w = 1.0; Using the inverse matrix of the camera's view x projection matrix does work for lighting but maybe it is incorrect for shadow calculation? EDIT: Light calculations for shadow including 'dist_to_light' // Work out the light position and direction in world space float3 light_position = float3(LightViewInverse._41, LightViewInverse._42, LightViewInverse._43); // Direction might need to be negated float3 light_direction = float3(-LightViewInverse._31, -LightViewInverse._32, -LightViewInverse._33); // Unnormalized light vector float3 dir_to_light = light_position - position; // Direction from vertex float dist_to_light = length(dir_to_light); // Normalise 'toLight' vector for lighting calculations dir_to_light = normalize(dir_to_light); EDIT2: These are the calculations for the moments (depth) //============================================= //---[Vertex Shaders]-------------------------- //============================================= DepthVSOutput depth_VS( float4 Position : POSITION, uniform float4x4 shadow_view, uniform float4x4 shadow_view_projection) { DepthVSOutput output = (DepthVSOutput)0; // First transform position into world space float4 position_world = mul(Position, World); output.position_screen = mul(position_world, shadow_view_projection); output.light_vec = mul(position_world, shadow_view).xyz; return output; } //============================================= //---[Pixel Shaders]--------------------------- //============================================= DepthPSOutput depth_PS(DepthVSOutput input) { DepthPSOutput output = (DepthPSOutput)0; // Work out the depth of this fragment from the light, normalized to [0, 1] float2 depth; depth.x = length(input.light_vec) / FarPlane; depth.y = depth.x * depth.x; // Flip depth values to avoid floating point inaccuracies depth.x = 1.0f - depth.x; depth.y = 1.0f - depth.y; output.depth = depth.xyxy; return output; } EDIT 3: I have tried the folloiwng: float4 pp; pp.xy = input.PositionClone.xy; // Use 'x' and 'y' components already homogenized for uv coordinates above pp.z = tex2D(DepthSampler, texCoord).r; // No need to homogenize 'z' component pp.z = 1.0 - pp.z; pp.w = 1.0; // 1.0 = position.w / position.w // Determine the depth of the pixel with respect to the light float4x4 LightViewProjection = mul(LightView, LightProjection); float4x4 matViewToLightViewProj = mul(CameraViewProjectionInverse, LightViewProjection); float4 vPositionLightCS = mul(pp, matViewToLightViewProj); float fLightDepth = vPositionLightCS.z / vPositionLightCS.w; // Transform from light space to shadow map texture space. float2 vShadowTexCoord = 0.5 * vPositionLightCS.xy / vPositionLightCS.w + float2(0.5f, 0.5f); vShadowTexCoord.y = 1.0f - vShadowTexCoord.y; // Offset the coordinate by half a texel so we sample it correctly vShadowTexCoord += (0.5f / 512); //g_vShadowMapSize This suffers the same problem as the second picture. I have tried storing the depth based on the view x projection matrix: output.position_screen = mul(position_world, shadow_view_projection); //output.light_vec = mul(position_world, shadow_view); output.light_vec = output.position_screen; depth.x = input.light_vec.z / input.light_vec.w; This gives a shadow that has lots surface acne due to horrible floating point precision errors. Everything is lit correctly though. EDIT 4: Found an OpenGL based tutorial here I have followed it to the letter and it would seem that the uv coordinates for looking up the shadow map are incorrect. The source uses a scaled matrix to get the uv coordinates for the shadow map sampler /// <summary> /// The scale matrix is used to push the projected vertex into the 0.0 - 1.0 region. /// Similar in role to a * 0.5 + 0.5, where -1.0 < a < 1.0. /// <summary> const float4x4 ScaleMatrix = float4x4 ( 0.5, 0.0, 0.0, 0.0, 0.0, -0.5, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.5, 0.5, 0.5, 1.0 ); I had to negate the 0.5 for the y scaling (M22) in order for it to work but the shadowing is still not correct. Is this really the correct way to scale? float2 shadow_tex; shadow_tex.x = surf_tex.x * 0.5f + 0.5f; shadow_tex.y = surf_tex.y * -0.5f + 0.5f; The depth calculations are exactly the same as the source code yet they still do not work, which makes me believe something about the uv calculation above is incorrect.

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• Pure Java open-source libraries for portfolio selection (= constrained, non-linear optimization)?

  - by __roland__

  Does anyone know or has experience with a pure Java library to select portfolios or do some similar kinds of quadratic programming with constraints? There seems to be a lot of tools, as already discussed elsewhere - but what I would like to use is a pure Java implementation. Since I want to call the library from within another open-source software with a BSD-ish license I would prefer LGPL over GPL. Any help is appreciated. If you don't know such libraries, which is the most simple algorithm you would suggest to implement? It has to cope with an inequality constraint (all x_i = 0) and an equality constraint (sum of all x_i = 1).

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• Apply CSS Style on all elements except with a SPECIFIC ID

  - by Rajesh Paul

  CSS Code(what I need) <style> div[id!='div1']// I actually needed an inequality operator for NOT EQUAL TO { font-size:40px; } </style> HTML code <body> <div>abc</div> <div>def</div> <div id='div1'>ghi</div> </body> The CSS didn't work as I intended. I actually wanted to define the style for all <div>-elements except the one with id='div1'. How can I do that?

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• what is the fastest way to copy all data to a new larger hard drive?

  - by SUPER user

  I was certain this would have been covered before, but I cannot find an answer amongst all the almost-duplicates that come up; sorry if I've missed something obvious. I have a full 320gb disk inside my machine, a new 1tb disk to replace it, and a USB 2.0 chassis. It is only data on a single partition, no OS/apps involved, and the old drive will be kept somewhere as backup (no secure wiping etc). The simple option would be to put new disk in USB chassis, copy files, then swap them over. But for USB pen drives, reading is around 4x faster than writing. If the same is true for a USB SATA chassis (is it?) then it would be significantly faster to swap the drives first and read from the old drive over USB, right? Then the other consideration is that copying lots of files is usually slower than a single file of equivalent size. Is Windows 7 smart enough to do everything in a single lump like that, or is there specialised software that should be used instead? (Even if SATA-SATA copying is faster than involving USB, knowing what to do when it isn't an option is useful information.) Summary: Does a USB SATA chassis suffer from a read/write inequality? (like a USB pen drive does, but unlike a direct SATA connection) Can Windows 7 do sequential access? (I can't find confirmation if Robocopy does this.) Or is it necessary to use a bootable CD/USB with something like Clonezilla to achieve sequential copy speeds?

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• concurrency::accelerator_view

  - by Daniel Moth

  Overview We saw previously that accelerator represents a target for our C++ AMP computation or memory allocation and that there is a notion of a default accelerator. We ended that post by introducing how one can obtain accelerator_view objects from an accelerator object through the accelerator class's default_view property and the create_view method. The accelerator_view objects can be thought of as handles to an accelerator. You can also construct an accelerator_view given another accelerator_view (through the copy constructor or the assignment operator overload). Speaking of operator overloading, you can also compare (for equality and inequality) two accelerator_view objects between them to determine if they refer to the same underlying accelerator. We'll see later that when we use concurrency::array objects, the allocation of data takes place on an accelerator at array construction time, so there is a constructor overload that accepts an accelerator_view object. We'll also see later that a new concurrency::parallel_for_each function overload can take an accelerator_view object, so it knows on what target to execute the computation (represented by a lambda that the parallel_for_each also accepts). Beyond normal usage, accelerator_view is a quality of service concept that offers isolation to multiple "consumers" of an accelerator. If in your code you are accessing the accelerator from multiple threads (or, in general, from different parts of your app), then you'll want to create separate accelerator_view objects for each thread. flush, wait, and queuing_mode When you create an accelerator_view via the create_view method of the accelerator, you pass in an option of immediate or deferred, which are the two members of the queuing_mode enum. At any point you can access this value from the queuing_mode property of the accelerator_view. When the queuing_mode value is immediate (which is the default), any commands sent to the device such as kernel invocations and data transfers (e.g. parallel_for_each and copy, as we'll see in future posts), will get submitted as soon as the runtime sees fit (that is the definition of immediate). When the value of queuing_mode is deferred, the commands will be batched up. To send all buffered commands to the device for execution, there is a non-blocking flush method that you can call. If you wish to block until all the commands have been sent, there is a wait method you can call. Deferring is a more advanced scenario aimed at performance gains when you are submitting many device commands and you want to avoid the tiny overhead of flushing/submitting each command separately. Querying information Just like accelerator, accelerator_view exposes the is_debug and version properties. In fact, you can always access the accelerator object from the accelerator property on the accelerator_view class to access the accelerator interface we looked at previously. Interop with D3D (aka DX) In a later post I'll show an example of an app that uses C++ AMP to compute data that is used in pixel shaders. In those scenarios, you can benefit by integrating C++ AMP into your graphics pipeline and one of the building blocks for that is being able to use the same device context from both the compute kernel and the other shaders. You can do that by going from accelerator_view to device context (and vice versa), through part of our interop API in amp.h: *get_device, create_accelerator_view. More on those in a later post. Comments about this post by Daniel Moth welcome at the original blog.

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• Simple Program Question (TI-84 calculator)

  - by John

  I wrote a program (on my TI-84 calculator) to satisfy the following exercise: Write a program that will print all solutions of the inequality ax + b < c, where a, b, and c are entered by the user. In this program the domain of x will be a set of consecutive integers, where the smallest and largest members of the set will also be entered by the user. (Hint: Use a FOR . . . NEXT loop to test each integer from smallest to largest.) This is the code I have: :Input "A=",A :Input "B=",B :Input "C=",C :Disp "DOMAIN FOR X" :Input "MIN=",D :Input "MAX=",E :For(X,D,E,1) :If AX+B<C :Disp X :End I sort of figured it out by chance; I don't really know how 'If' works inside 'For.' I wanted to have it tell me if there is no solution, though, so I tried: :Input "A=",A :Input "B=",B :Input "C=",C :Disp "DOMAIN FOR X" :Input "MIN=",D :Input "MAX=",E :For(X,D,E,1) :If AX+B<C :Then :Disp X :Else :Disp "NO SOLUTION" :End But this returns the value for "MIN=" Why is this? Can anyone help me understand these work?

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• Abstracting boxed array structures in J

  - by estanford

  I've been working on a J function for a while, that's supposed to scan a list and put consecutive copies of an element into separate, concatenated boxes. My efforts have taken me as far as the function (<;. 2) ((2&(~:/\)),1:) which tests successive list entries for inequality, returns a list of boolean values, and cuts the list into boxes that end each time the number 1 appears. Here's an example application: (<;. 2) ((2&(~:/\)),1:) 1 2 3 3 3 4 1 1 1 +-+-+-----+-+-----+ |1|1|0 0 1|1|0 0 1| +-+-+-----+-+-----+ The task would be finished if I could then replace all those booleans with their corresponding values in the input argument. I've been looking for some kind of mystery function that would let me do something like final =: mysteryfunction @ (<;. 2) ((2&(~:/\)),1:) final 1 2 3 3 3 4 1 1 1 +-+-+-----+-+-----+ |1|2|3 3 3|4|1 1 1| +-+-+-----+-+-----+ In an ideal situation, there would be some way to abstractly represent the nesting pattern generated by (<;. 2) ((2&(~:/\)),1:) to the original input list. (i.e. "This boxed array over here has the first element boxed at depth one, the second element boxed at depth one, the third, fourth, and fifth elements boxed together at depth one,..., so take that unboxed list over there and box it up the same way.") I tried fooling around with ;. , S: , L:, L. and &. to produce that behavior, but I haven't had much luck. Is there some kind of operator or principle I'm missing that could make this happen? It wouldn't surprise me if I were overthinking the whole issue, but I'm running out of ideas.

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• Practice of checking 'trueness' or 'equality' in conditional statements - does it really make sense?

  - by Senthil

  I remember many years back, when I was in school, one of my computer science teachers taught us that it was better to check for 'trueness' or 'equality' of a condition and not the negative stuff like 'inequality'. Let me elaborate - If a piece of conditional code can be written by checking whether an expression is true or false, we should check the 'trueness'. Example: Finding out whether a number is odd - it can be done in two ways: if ( num % 2 != 0 ) { // Number is odd } or if ( num % 2 == 1 ) { // Number is odd } When I was beginning to code, I knew that num % 2 == 0 implies the number is even, so I just put a ! there to check if it is odd. But he was like 'Don't check NOT conditions. Have the practice of checking the 'trueness' or 'equality' of conditions whenever possible.' And he recommended that I use the second piece of code. I am not for or against either but I just wanted to know - what difference does it make? Please don't reply 'Technically the output will be the same' - we ALL know that. Is it a general programming practice or is it his own programming practice that he is preaching to others?

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• negative values in integer programming model

  - by Lucia

  I'm new at using the glpk tool, and after writing a model for certain integer problem and running the solver (glpsol) i get negative values in some constraint that shouldn't be negative at all: No.Row name Activity Lower bound Upper bound 8 act[1] 0 -0 9 act[2] -3 -0 10 act[2] -2 -0 That constraint is defined like this: act{j in J}: sum{i in I} d[i,j] <= y[j]*m; where the sets and variables used are like this: param m, integer, 0; param n, integer, 0; set I := 1..m; set J := 1..n; var y{j in J}, binary; As the upper bound is negative, i think the problem may be in the y[j]*m parte, of the right side of the inequality.. perhaps something with the multiplication of binarys? or that the j in that side of the constrait is undefined? i dont know... i would be greatly grateful if someone can help me with this! :) and excuse for my bad english thanks in advance!

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• Practice of checking 'trueness' or 'equality' of conditional statements - does it really make sense?

  - by senthilkumar1033

  I remember many years back, when I was in school, one of my computer science teachers taught us that it was better to check for 'trueness' or 'equality' of a condition and not the negative stuff like 'inequality'. Let me elaborate - If a piece of conditional code can be written by checking whether an expression is true or false, we should check the 'trueness'. Example: Finding out whether a number is odd - it can be done in two ways: if ( num % 2 != 0 ) { // Number is odd } or if ( num % 2 == 1 ) { // Number is odd } When I was beginning to code, I knew that num % 2 == 0 implies the number is even, so I just put a ! there to check if it is odd. But he was like 'Don't check NOT conditions. Have the practice of checking the 'trueness' or 'equality' of conditions whenever possible.' And he recommended that I use the second piece of code. I am not for or against either but I just wanted to know - what difference does it make? Please don't reply 'Technically the output will be the same' - we ALL know that. Is it a general programming practice or is it his own programming practice that he is preaching to others?

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• concurrency::accelerator

  - by Daniel Moth

  Overview An accelerator represents a "target" on which C++ AMP code can execute and where data can reside. Typically (but not necessarily) an accelerator is a GPU device. Accelerators are represented in C++ AMP as objects of the accelerator class. For many scenarios, you do not need to obtain an accelerator object, since the runtime has a notion of a default accelerator, which is what it thinks is the best one in the system. Examples where you need to deal with accelerator objects are if you need to pick your own accelerator (based on your specific criteria), or if you need to use more than one accelerators from your app. Construction and operator usage You can query and obtain a std::vector of all the accelerators on your system, which the runtime discovers on startup. Beyond enumerating accelerators, you can also create one directly by passing to the constructor a system-wide unique path to a device if you know it (i.e. the “Device Instance Path” property for the device in Device Manager), e.g. accelerator acc(L"PCI\\VEN_1002&DEV_6898&SUBSYS_0B001002etc"); There are some predefined strings (for predefined accelerators) that you can pass to the accelerator constructor (and there are corresponding constants for those on the accelerator class itself, so you don’t have to hardcode them every time). Examples are the following: accelerator::default_accelerator represents the default accelerator that the C++ AMP runtime picks for you if you don’t pick one (the heuristics of how it picks one will be covered in a future post). Example: accelerator acc; accelerator::direct3d_ref represents the reference rasterizer emulator that simulates a direct3d device on the CPU (in a very slow manner). This emulator is available on systems with Visual Studio installed and is useful for debugging. More on debugging in general in future posts. Example: accelerator acc(accelerator::direct3d_ref); accelerator::direct3d_warp represents a target that I will cover in future blog posts. Example: accelerator acc(accelerator::direct3d_warp); accelerator::cpu_accelerator represents the CPU. In this first release the only use of this accelerator is for using the staging arrays technique that I'll cover separately. Example: accelerator acc(accelerator::cpu_accelerator); You can also create an accelerator by shallow copying another accelerator instance (via the corresponding constructor) or simply assigning it to another accelerator instance (via the operator overloading of =). Speaking of operator overloading, you can also compare (for equality and inequality) two accelerator objects between them to determine if they refer to the same underlying device. Querying accelerator characteristics Given an accelerator object, you can access its description, version, device path, size of dedicated memory in KB, whether it is some kind of emulator, whether it has a display attached, whether it supports double precision, and whether it was created with the debugging layer enabled for extensive error reporting. Below is example code that accesses some of the properties; in your real code you'd probably be checking one or more of them in order to pick an accelerator (or check that the default one is good enough for your specific workload): void inspect_accelerator(concurrency::accelerator acc) { std::wcout << "New accelerator: " << acc.description << std::endl; std::wcout << "is_debug = " << acc.is_debug << std::endl; std::wcout << "is_emulated = " << acc.is_emulated << std::endl; std::wcout << "dedicated_memory = " << acc.dedicated_memory << std::endl; std::wcout << "device_path = " << acc.device_path << std::endl; std::wcout << "has_display = " << acc.has_display << std::endl; std::wcout << "version = " << (acc.version >> 16) << '.' << (acc.version & 0xFFFF) << std::endl; } accelerator_view In my next blog post I'll cover a related class: accelerator_view. Suffice to say here that each accelerator may have from 1..n related accelerator_view objects. You can get the accelerator_view from an accelerator via the default_view property, or create new ones by invoking the create_view method that creates an accelerator_view object for you (by also accepting a queuing_mode enum value of deferred or immediate that we'll also explore in the next blog post). Comments about this post by Daniel Moth welcome at the original blog.

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• Investigation: Can different combinations of components effect Dataflow performance?

  - by jamiet

  Introduction The Dataflow task is one of the core components (if not the core component) of SQL Server Integration Services (SSIS) and often the most misunderstood. This is not surprising, its an incredibly complicated beast and we’re abstracted away from that complexity via some boxes that go yellow red or green and that have some lines drawn between them. Example dataflow In this blog post I intend to look under that facade and get into some of the nuts and bolts of the Dataflow Task by investigating how the decisions we make when building our packages can affect performance. I will do this by comparing the performance of three dataflows that all have the same input, all produce the same output, but which all operate slightly differently by way of having different transformation components. I also want to use this blog post to challenge a common held opinion that I see perpetuated over and over again on the SSIS forum. That is, that people assume adding components to a dataflow will be detrimental to overall performance. Its not surprising that people think this –it is intuitive to think that more components means more work- however this is not a view that I share. I have always been of the opinion that there are many factors affecting dataflow duration and the number of components is actually one of the less important ones; having said that I have never proven that assertion and that is one reason for this investigation. I have actually seen evidence that some people think dataflow duration is simply a function of number of rows and number of components. I’ll happily call that one out as a myth even without any investigation!  The Setup I have a 2GB datafile which is a list of 4731904 (~4.7million) customer records with various attributes against them and it contains 2 columns that I am going to use for categorisation: [YearlyIncome] [BirthDate] The data file is a SSIS raw format file which I chose to use because it is the quickest way of getting data into a dataflow and given that I am testing the transformations, not the source or destination adapters, I want to minimise external influences as much as possible. In the test I will split the customers according to month of birth (12 of those) and whether or not their yearly income is above or below 50000 (2 of those); in other words I will be splitting them into 24 discrete categories and in order to do it I shall be using different combinations of SSIS’ Conditional Split and Derived Column transformation components. The 24 datapaths that occur will each input to a rowcount component, again because this is the least resource intensive means of terminating a datapath. The test is being carried out on a Dell XPS Studio laptop with a quad core (8 logical Procs) Intel Core i7 at 1.73GHz and Samsung SSD hard drive. Its running SQL Server 2008 R2 on Windows 7. The Variables Here are the three combinations of components that I am going to test:     One Conditional Split - A single Conditional Split component CSPL Split by Month of Birth and income category that will use expressions on [YearlyIncome] & [BirthDate] to send each row to one of 24 outputs. This next screenshot displays the expression logic in use: Derived Column & Conditional Split - A Derived Column component DER Income Category that adds a new column [IncomeCategory] which will contain one of two possible text values {“LessThan50000”,”GreaterThan50000”} and uses [YearlyIncome] to determine which value each row should get. A Conditional Split component CSPL Split by Month of Birth and Income Category then uses that new column in conjunction with [BirthDate] to determine which of the same 24 outputs to send each row to. Put more simply, I am separating the Conditional Split of #1 into a Derived Column and a Conditional Split. The next screenshots display the expression logic in use: DER Income Category         CSPL Split by Month of Birth and Income Category       Three Conditional Splits - A Conditional Split component that produces two outputs based on [YearlyIncome], one for each Income Category. Each of those outputs will go to a further Conditional Split that splits the input into 12 outputs, one for each month of birth (identical logic in each). In this case then I am separating the single Conditional Split of #1 into three Conditional Split components. The next screenshots display the expression logic in use: CSPL Split by Income Category         CSPL Split by Month of Birth 1& 2       Each of these combinations will provide an input to one of the 24 rowcount components, just the same as before. For illustration here is a screenshot of the dataflow containing three Conditional Split components: As you can these dataflows have a fair bit of work to do and remember that they’re doing that work for 4.7million rows. I will execute each dataflow 10 times and use the average for comparison. I foresee three possible outcomes: The dataflow containing just one Conditional Split (i.e. #1) will be quicker There is no significant difference between any of them One of the two dataflows containing multiple transformation components will be quicker Regardless of which of those outcomes come to pass we will have learnt something and that makes this an interesting test to carry out. Note that I will be executing the dataflows using dtexec.exe rather than hitting F5 within BIDS. The Results and Analysis The table below shows all of the executions, 10 for each dataflow. It also shows the average for each along with a standard deviation. All durations are in seconds. I’m pasting a screenshot because I frankly can’t be bothered with the faffing about needed to make a presentable HTML table. It is plain to see from the average that the dataflow containing three conditional splits is significantly faster, the other two taking 43% and 52% longer respectively. This seems strange though, right? Why does the dataflow containing the most components outperform the other two by such a big margin? The answer is actually quite logical when you put some thought into it and I’ll explain that below. Before progressing, a side note. The standard deviation for the “Three Conditional Splits” dataflow is orders of magnitude smaller – indicating that performance for this dataflow can be predicted with much greater confidence too. The Explanation I refer you to the screenshot above that shows how CSPL Split by Month of Birth and salary category in the first dataflow is setup. Observe that there is a case for each combination of Month Of Date and Income Category – 24 in total. These expressions get evaluated in the order that they appear and hence if we assume that Month of Date and Income Category are uniformly distributed in the dataset we can deduce that the expected number of expression evaluations for each row is 12.5 i.e. 1 (the minimum) + 24 (the maximum) divided by 2 = 12.5. Now take a look at the screenshots for the second dataflow. We are doing one expression evaluation in DER Income Category and we have the same 24 cases in CSPL Split by Month of Birth and Income Category as we had before, only the expression differs slightly. In this case then we have 1 + 12.5 = 13.5 expected evaluations for each row – that would account for the slightly longer average execution time for this dataflow. Now onto the third dataflow, the quick one. CSPL Split by Income Category does a maximum of 2 expression evaluations thus the expected number of evaluations per row is 1.5. CSPL Split by Month of Birth 1 & CSPL Split by Month of Birth 2 both have less work to do than the previous Conditional Split components because they only have 12 cases to test for thus the expected number of expression evaluations is 6.5 There are two of them so total expected number of expression evaluations for this dataflow is 6.5 + 6.5 + 1.5 = 14.5. 14.5 is still more than 12.5 & 13.5 though so why is the third dataflow so much quicker? Simple, the conditional expressions in the first two dataflows have two boolean predicates to evaluate – one for Income Category and one for Month of Birth; the expressions in the Conditional Split in the third dataflow however only have one predicate thus they are doing a lot less work. To sum up, the difference in execution times can be attributed to the difference between: MONTH(BirthDate) == 1 && YearlyIncome <= 50000 and MONTH(BirthDate) == 1 In the first two dataflows YearlyIncome <= 50000 gets evaluated an average of 12.5 times for every row whereas in the third dataflow it is evaluated once and once only. Multiply those 11.5 extra operations by 4.7million rows and you get a significant amount of extra CPU cycles – that’s where our duration difference comes from. The Wrap-up The obvious point here is that adding new components to a dataflow isn’t necessarily going to make it go any slower, moreover you may be able to achieve significant improvements by splitting logic over multiple components rather than one. Performance tuning is all about reducing the amount of work that needs to be done and that doesn’t necessarily mean use less components, indeed sometimes you may be able to reduce workload in ways that aren’t immediately obvious as I think I have proven here. Of course there are many variables in play here and your mileage will most definitely vary. I encourage you to download the package and see if you get similar results – let me know in the comments. The package contains all three dataflows plus a fourth dataflow that will create the 2GB raw file for you (you will also need the [AdventureWorksDW2008] sample database from which to source the data); simply disable all dataflows except the one you want to test before executing the package and remember, execute using dtexec, not within BIDS. If you want to explore dataflow performance tuning in more detail then here are some links you might want to check out: Inequality joins, Asynchronous transformations and Lookups Destination Adapter Comparison Don’t turn the dataflow into a cursor SSIS Dataflow – Designing for performance (webinar) Any comments? Let me know! @Jamiet

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• Linked List manipulation, issues retrieving data c++

  - by floatfil

  I'm trying to implement some functions to manipulate a linked list. The implementation is a template typename T and the class is 'List' which includes a 'head' pointer and also a struct: struct Node { // the node in a linked list T* data; // pointer to actual data, operations in T Node* next; // pointer to a Node }; Since it is a template, and 'T' can be any data, how do I go about checking the data of a list to see if it matches the data input into the function? The function is called 'retrieve' and takes two parameters, the data and a pointer: bool retrieve(T target, T*& ptr); // This is the prototype we need to use for the project "bool retrieve : similar to remove, but not removed from list. If there are duplicates in the list, the first one encountered is retrieved. Second parameter is unreliable if return value is false. E.g., " Employee target("duck", "donald"); success = company1.retrieve(target, oneEmployee); if (success) { cout << "Found in list: " << *oneEmployee << endl; } And the function is called like this: company4.retrieve(emp3, oneEmployee) So that when you cout *oneEmployee, you'll get the data of that pointer (in this case the data is of type Employee). (Also, this is assuming all data types have the apropriate overloaded operators) I hope this makes sense so far, but my issue is in comparing the data in the parameter and the data while going through the list. (The data types that we use all include overloads for equality operators, so oneData == twoData is valid) This is what I have so far: template <typename T> bool List<T>::retrieve(T target , T*& ptr) { List<T>::Node* dummyPtr = head; // point dummy pointer to what the list's head points to for(;;) { if (*dummyPtr->data == target) { // EDIT: it now compiles, but it breaks here and I get an Access Violation error. ptr = dummyPtr->data; // set the parameter pointer to the dummy pointer return true; // return true } else { dummyPtr = dummyPtr->next; // else, move to the next data node } } return false; } Here is the implementation for the Employee class: //-------------------------- constructor ----------------------------------- Employee::Employee(string last, string first, int id, int sal) { idNumber = (id >= 0 && id <= MAXID? id : -1); salary = (sal >= 0 ? sal : -1); lastName = last; firstName = first; } //-------------------------- destructor ------------------------------------ // Needed so that memory for strings is properly deallocated Employee::~Employee() { } //---------------------- copy constructor ----------------------------------- Employee::Employee(const Employee& E) { lastName = E.lastName; firstName = E.firstName; idNumber = E.idNumber; salary = E.salary; } //-------------------------- operator= --------------------------------------- Employee& Employee::operator=(const Employee& E) { if (&E != this) { idNumber = E.idNumber; salary = E.salary; lastName = E.lastName; firstName = E.firstName; } return *this; } //----------------------------- setData ------------------------------------ // set data from file bool Employee::setData(ifstream& inFile) { inFile >> lastName >> firstName >> idNumber >> salary; return idNumber >= 0 && idNumber <= MAXID && salary >= 0; } //------------------------------- < ---------------------------------------- // < defined by value of name bool Employee::operator<(const Employee& E) const { return lastName < E.lastName || (lastName == E.lastName && firstName < E.firstName); } //------------------------------- <= ---------------------------------------- // < defined by value of inamedNumber bool Employee::operator<=(const Employee& E) const { return *this < E || *this == E; } //------------------------------- > ---------------------------------------- // > defined by value of name bool Employee::operator>(const Employee& E) const { return lastName > E.lastName || (lastName == E.lastName && firstName > E.firstName); } //------------------------------- >= ---------------------------------------- // < defined by value of name bool Employee::operator>=(const Employee& E) const { return *this > E || *this == E; } //----------------- operator == (equality) ---------------- // if name of calling and passed object are equal, // return true, otherwise false // bool Employee::operator==(const Employee& E) const { return lastName == E.lastName && firstName == E.firstName; } //----------------- operator != (inequality) ---------------- // return opposite value of operator== bool Employee::operator!=(const Employee& E) const { return !(*this == E); } //------------------------------- << --------------------------------------- // display Employee object ostream& operator<<(ostream& output, const Employee& E) { output << setw(4) << E.idNumber << setw(7) << E.salary << " " << E.lastName << " " << E.firstName << endl; return output; } I will include a check for NULL pointer but I just want to get this working and will test it on a list that includes the data I am checking. Thanks to whoever can help and as usual, this is for a course so I don't expect or want the answer, but any tips as to what might be going wrong will help immensely!

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